Analytics Engineer Visa Sponsorship Jobs in New York
Analytics engineer roles in New York are concentrated across financial services firms in Manhattan, media and ad-tech companies, and a growing base of enterprise tech employers. Major sponsors include JPMorgan Chase, Bloomberg, Spotify, and Conde Nast. New York's density of data-driven organizations makes it one of the most active states for analytics engineer visa sponsorship.
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Requisition ID
95195
Department
Tech Data AI Ventures
Job Function
Tech Data AI Ventures
Location
New York, New York, United States
Role Location Designation
Hybrid - 3 days per week
Technology, Data, AI and Ventures:
Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life's businesses. We build solutions that power how we serve policy owners, agents, advisors and employees while delivering measurable business outcomes.
Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader, access to leading-edge technologies and the opportunity to help shape how a world-class financial services company competes in the AI era - all backed by the stability and purpose of a mutual company built to last.
Role Overview
The Senior Associate, Analytics Engineer is a practitioner who designs and implements scalable analytics engineering solutions with a high degree of independence. You lead your own workstreams end-to-end — from source alignment and data modeling through testing, deployment, and quality monitoring — engaging with the Analytics Engineering Lead and senior engineers for input on the most complex architectural decisions.
You are technically strong, self-directed, and effective at translating business requirements into well-structured engineering work. You exercise independent judgment in selecting approaches and techniques, engage directly with business stakeholders to understand requirements, and provide input into team-level goals and delivery planning. You advise peers on data modeling and analytics engineering best practices, and actively contribute to improving the team’s SDLC practices.
What You’ll Do
Deep-Dive Exploratory Analysis
- Conduct end-to-end deep dives into large datasets to isolate variables driving user behavior, financial anomalies, or operational trends.
Formulate & Test Hypotheses
- Proactively generate hypotheses based on business problems and run statistical tests to prove or disprove them.
Deconstruct Business Metrics
- Move past surface-level KPIs (e.g., "Revenue is down 5%") to identify the exact levers causing the shift (e.g., "A localized drop in conversion rate among cohort X during checkout step 2").
Present Strategic Recommendations
- Translate complex technical findings into narrative-driven, actionable business strategies for executive stakeholders.
Collaborate on Experiments
- Partner with product or marketing teams to design, track, and interpret rigorous A/B tests and experimentation models.
Pipeline Design & Data Product Delivery
-
Lead, with support from the Analytics Engineering Lead, the design and implementation of scalable dbt transformation pipelines across Databricks, Postgres and Bigquery — covering layered modeling (staging / intermediate / mart), incremental strategies, and source contract definitions.
-
Design and build well-tested, documented data products — dimensional models, aggregates, and feature tables.
-
Develop solutions to complex data transformation problems using advanced SQL and Python, selecting the right approach based on evaluation, judgment, and the performance and maintainability requirements of the platform.
-
Optimize and tune transformation pipelines for performance, cost efficiency, and incremental processing at scale — independently identifying bottlenecks and driving improvements.
-
Own your data products end-to-end: source alignment, modeling, testing, documentation, deployment, and post-release monitoring, with awareness of downstream BI and AI/ML dependencies.
Data Quality, Governance & SDLC
-
Lead, with support from senior engineers, the availability, usability, integrity, and security of data within your domain — ensuring data is consistent, trustworthy, and governed in accordance with enterprise standards.
-
Implement robust dbt test frameworks, source freshness checks, and data quality monitoring patterns that make pipeline health observable and failures diagnosable.
-
Apply governance standards at the analytics layer: column-level PII tagging, access control integration, and lineage documentation that supports the enterprise data catalog.
-
Lead efforts to improve SDLC practices within the team — contributing to and helping establish CI/CD pipelines, automated testing, branching conventions, and PR review standards.
-
Maintain data catalog entries for all owned assets: lineage, ownership, grain documentation, and business glossary alignment.
Innovation & Pattern Development
-
Develop and maintain reusable macro libraries and dbt modeling patterns that enforce consistency and accelerate delivery across the analytics engineering surface.
-
Participate in semantic layer development — building MetricFlow-based metric definitions that provide a governed, authoritative source of business logic decoupled from downstream consumption.
-
Contribute to self-healing pipeline patterns and agentic pipeline construction approaches — prototyping and implementing automated anomaly detection, quality remediation, and LLM-assisted transformation generation.
-
Support context graph construction that captures relationships between business entities and data assets, enabling richer AI reasoning and cross-domain signal integration.
-
Stay current with the dbt ecosystem, Databricks and BigQuery platform releases, and the broader analytics engineering field — bringing concrete, evaluated recommendations back to the team.
Stakeholder Engagement & Collaboration
-
Engage directly with business stakeholders, data scientists, and ML engineers to understand data requirements — translating them into well-scoped Jira stories with clear acceptance criteria, grain definitions, and delivery estimates.
-
Partner with Integration Services on ingestion design to ensure source data arrives in shapes that are transformation-ready, correctly typed, and well-governed before reaching the analytics layer.
-
Collaborate with data stewards across NYL to resolve data quality issues at the source — driving shared accountability for data integrity rather than working around upstream problems.
-
Communicate technical decisions, modeling trade-offs, and delivery status clearly to the Analytics Engineering Lead and cross-functional partners, adapting style and depth for technical and non-technical audiences.
-
Advise junior engineers on data modeling approaches, dbt patterns, SQL craft, and analytics engineering best practices through code review and pair-modeling sessions.
What You’ll Bring
Required
-
6+ years of progressive data engineering experience with a strong focus on analytics engineering and transformation pipeline development in production cloud environments.
-
Strong command of advanced SQL — window functions, CTEs, performance optimization, and complex multi-source joins — and solid Python proficiency for data engineering tasks.
-
Hands-on dbt experience: layered modeling, incremental models, source definitions, singular and generic tests, macros, and multi-environment project configuration.
-
Experience with cloud data platforms — Databricks and/or BigQuery required; experience with Snowflake or Redshift a plus.
-
Understanding of ELT patterns, dimensional modeling, and the design of scalable analytical data products with clear grain, ownership, and consumer contracts.
-
Proficiency with Git and collaborative development workflows: branching strategy, PR review, and CI/CD pipeline integration.
-
Experience working in Agile/Scrum delivery environments with structured sprint planning, backlog grooming, and milestone tracking.
-
Clear written and verbal communication skills — able to explain technical decisions, document data products, and engage effectively with both engineering and business stakeholders.
Preferred
-
Exposure to semantic layer tooling — dbt Semantic Layer / MetricFlow, Looker LookML, or equivalent — and experience building metric definitions consumed by BI or AI systems.
-
Familiarity with agentic AI concepts and interest in applying LLM-assisted tooling to pipeline construction, data quality remediation, or transformation pattern generation.
-
Experience with data observability tooling (Monte Carlo, Anomalo, or dbt built-in monitoring patterns) and building self-monitoring pipeline patterns.
-
Exposure to graph data models, knowledge graphs, or context graph construction for AI or analytics use cases.
-
Experience with data catalog and lineage platforms (Dataplex, DataHub, Alation) and column-level governance tagging practices.
-
Insurance or financial services industry experience, with familiarity with data privacy standards and enterprise compliance requirements.
Job Level: LEVELPF4
Pay Transparency
-
Salary Range: $124,000-$177,000
-
Overtime eligible: Exempt
-
Discretionary bonus eligible: Yes
-
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Company Overview
At New York Life, our 180-year legacy of purpose and integrity fuels our future. As we evolve into a more technology-, data-, and AI-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities—inviting bold thinking, collaborative problem-solving, and purpose-driven innovation. Here, you’ll find the rare balance of long-standing stability and forward momentum, supported by an inclusive team that honors tradition while embracing progress.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and deliver solutions that matter. Your ideas drive what’s next, and your growth powers it.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work. Click here to discover more about our comprehensive benefit options or visit our NYL Benefits Site.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities. Click here to learn more about New York Life’s leadership in this space.
Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners. To learn more about career opportunities at New York Life, please visit the Careers page of www.NewYorkLife.com.
Job Requisition ID: 95195
Job Description
Requisition ID
95195
Department
Tech Data AI Ventures
Job Function
Tech Data AI Ventures
Location
New York, New York, United States
Role Location Designation
Hybrid - 3 days per week
Technology, Data, AI and Ventures:
Within the Tech, Data, AI, Ventures (TDAV) organization, our work is guided by a shared vision: deploying the power of technology, data, AI and ventures to accelerate sustainable competitive advantage for New York Life's businesses. We build solutions that power how we serve policy owners, agents, advisors and employees while delivering measurable business outcomes.
Across technology, data, AI, cyber, product, digital experience, architecture and infrastructure, TDAV combines the scale and investment of an industry leader, access to leading-edge technologies and the opportunity to help shape how a world-class financial services company competes in the AI era - all backed by the stability and purpose of a mutual company built to last.
Role Overview
The Senior Associate, Analytics Engineer is a practitioner who designs and implements scalable analytics engineering solutions with a high degree of independence. You lead your own workstreams end-to-end — from source alignment and data modeling through testing, deployment, and quality monitoring — engaging with the Analytics Engineering Lead and senior engineers for input on the most complex architectural decisions.
You are technically strong, self-directed, and effective at translating business requirements into well-structured engineering work. You exercise independent judgment in selecting approaches and techniques, engage directly with business stakeholders to understand requirements, and provide input into team-level goals and delivery planning. You advise peers on data modeling and analytics engineering best practices, and actively contribute to improving the team’s SDLC practices.
What You’ll Do
Deep-Dive Exploratory Analysis
- Conduct end-to-end deep dives into large datasets to isolate variables driving user behavior, financial anomalies, or operational trends.
Formulate & Test Hypotheses
- Proactively generate hypotheses based on business problems and run statistical tests to prove or disprove them.
Deconstruct Business Metrics
- Move past surface-level KPIs (e.g., "Revenue is down 5%") to identify the exact levers causing the shift (e.g., "A localized drop in conversion rate among cohort X during checkout step 2").
Present Strategic Recommendations
- Translate complex technical findings into narrative-driven, actionable business strategies for executive stakeholders.
Collaborate on Experiments
- Partner with product or marketing teams to design, track, and interpret rigorous A/B tests and experimentation models.
Pipeline Design & Data Product Delivery
-
Lead, with support from the Analytics Engineering Lead, the design and implementation of scalable dbt transformation pipelines across Databricks, Postgres and Bigquery — covering layered modeling (staging / intermediate / mart), incremental strategies, and source contract definitions.
-
Design and build well-tested, documented data products — dimensional models, aggregates, and feature tables.
-
Develop solutions to complex data transformation problems using advanced SQL and Python, selecting the right approach based on evaluation, judgment, and the performance and maintainability requirements of the platform.
-
Optimize and tune transformation pipelines for performance, cost efficiency, and incremental processing at scale — independently identifying bottlenecks and driving improvements.
-
Own your data products end-to-end: source alignment, modeling, testing, documentation, deployment, and post-release monitoring, with awareness of downstream BI and AI/ML dependencies.
Data Quality, Governance & SDLC
-
Lead, with support from senior engineers, the availability, usability, integrity, and security of data within your domain — ensuring data is consistent, trustworthy, and governed in accordance with enterprise standards.
-
Implement robust dbt test frameworks, source freshness checks, and data quality monitoring patterns that make pipeline health observable and failures diagnosable.
-
Apply governance standards at the analytics layer: column-level PII tagging, access control integration, and lineage documentation that supports the enterprise data catalog.
-
Lead efforts to improve SDLC practices within the team — contributing to and helping establish CI/CD pipelines, automated testing, branching conventions, and PR review standards.
-
Maintain data catalog entries for all owned assets: lineage, ownership, grain documentation, and business glossary alignment.
Innovation & Pattern Development
-
Develop and maintain reusable macro libraries and dbt modeling patterns that enforce consistency and accelerate delivery across the analytics engineering surface.
-
Participate in semantic layer development — building MetricFlow-based metric definitions that provide a governed, authoritative source of business logic decoupled from downstream consumption.
-
Contribute to self-healing pipeline patterns and agentic pipeline construction approaches — prototyping and implementing automated anomaly detection, quality remediation, and LLM-assisted transformation generation.
-
Support context graph construction that captures relationships between business entities and data assets, enabling richer AI reasoning and cross-domain signal integration.
-
Stay current with the dbt ecosystem, Databricks and BigQuery platform releases, and the broader analytics engineering field — bringing concrete, evaluated recommendations back to the team.
Stakeholder Engagement & Collaboration
-
Engage directly with business stakeholders, data scientists, and ML engineers to understand data requirements — translating them into well-scoped Jira stories with clear acceptance criteria, grain definitions, and delivery estimates.
-
Partner with Integration Services on ingestion design to ensure source data arrives in shapes that are transformation-ready, correctly typed, and well-governed before reaching the analytics layer.
-
Collaborate with data stewards across NYL to resolve data quality issues at the source — driving shared accountability for data integrity rather than working around upstream problems.
-
Communicate technical decisions, modeling trade-offs, and delivery status clearly to the Analytics Engineering Lead and cross-functional partners, adapting style and depth for technical and non-technical audiences.
-
Advise junior engineers on data modeling approaches, dbt patterns, SQL craft, and analytics engineering best practices through code review and pair-modeling sessions.
What You’ll Bring
Required
-
6+ years of progressive data engineering experience with a strong focus on analytics engineering and transformation pipeline development in production cloud environments.
-
Strong command of advanced SQL — window functions, CTEs, performance optimization, and complex multi-source joins — and solid Python proficiency for data engineering tasks.
-
Hands-on dbt experience: layered modeling, incremental models, source definitions, singular and generic tests, macros, and multi-environment project configuration.
-
Experience with cloud data platforms — Databricks and/or BigQuery required; experience with Snowflake or Redshift a plus.
-
Understanding of ELT patterns, dimensional modeling, and the design of scalable analytical data products with clear grain, ownership, and consumer contracts.
-
Proficiency with Git and collaborative development workflows: branching strategy, PR review, and CI/CD pipeline integration.
-
Experience working in Agile/Scrum delivery environments with structured sprint planning, backlog grooming, and milestone tracking.
-
Clear written and verbal communication skills — able to explain technical decisions, document data products, and engage effectively with both engineering and business stakeholders.
Preferred
-
Exposure to semantic layer tooling — dbt Semantic Layer / MetricFlow, Looker LookML, or equivalent — and experience building metric definitions consumed by BI or AI systems.
-
Familiarity with agentic AI concepts and interest in applying LLM-assisted tooling to pipeline construction, data quality remediation, or transformation pattern generation.
-
Experience with data observability tooling (Monte Carlo, Anomalo, or dbt built-in monitoring patterns) and building self-monitoring pipeline patterns.
-
Exposure to graph data models, knowledge graphs, or context graph construction for AI or analytics use cases.
-
Experience with data catalog and lineage platforms (Dataplex, DataHub, Alation) and column-level governance tagging practices.
-
Insurance or financial services industry experience, with familiarity with data privacy standards and enterprise compliance requirements.
Job Level: LEVELPF4
Pay Transparency
-
Salary Range: $124,000-$177,000
-
Overtime eligible: Exempt
-
Discretionary bonus eligible: Yes
-
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Company Overview
At New York Life, our 180-year legacy of purpose and integrity fuels our future. As we evolve into a more technology-, data-, and AI-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities—inviting bold thinking, collaborative problem-solving, and purpose-driven innovation. Here, you’ll find the rare balance of long-standing stability and forward momentum, supported by an inclusive team that honors tradition while embracing progress.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and deliver solutions that matter. Your ideas drive what’s next, and your growth powers it.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work. Click here to discover more about our comprehensive benefit options or visit our NYL Benefits Site.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities. Click here to learn more about New York Life’s leadership in this space.
Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners. To learn more about career opportunities at New York Life, please visit the Careers page of www.NewYorkLife.com.
Job Requisition ID: 95195
Analytics Engineer Job Roles in New York
See all 311+ Analytics Engineer Jobs in New York
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Search Analytics Engineer Jobs in New YorkAnalytics Engineer Jobs in New York: Frequently Asked Questions
Which companies sponsor visas for analytics engineers in New York?
Financial institutions like JPMorgan Chase, Goldman Sachs, and Citigroup are among the most active H-1B visa sponsors for analytics engineering roles in New York. Tech-forward media and ad-tech companies, including Bloomberg, Spotify, and Verizon Media, also file regularly. Enterprise software and consulting firms with large New York offices, such as Deloitte and Accenture, sponsor analytics engineers as well.
Which visa types are most common for analytics engineer roles in New York?
The H-1B is the most common visa for analytics engineers in New York, as the role typically requires a bachelor's degree or higher in computer science, data science, statistics, or a related field, qualifying it as a specialty occupation. Candidates already holding OPT or STEM OPT through a U.S. university can work while H-1B sponsorship is pursued. The O-1A is an option for those with a demonstrable record of exceptional achievement in data or engineering fields.
Which cities in New York have the most analytics engineer sponsorship jobs?
Manhattan accounts for the overwhelming majority of analytics engineer sponsorship activity in New York, driven by finance, media, advertising technology, and enterprise tech employers. Brooklyn's tech corridor, particularly around DUMBO and Downtown Brooklyn, has grown as a secondary hub. Albany and Buffalo have a smaller but present concentration tied to state government data initiatives and regional healthcare systems.
How to find analytics engineer visa sponsorship jobs in New York?
Migrate Mate filters job listings specifically by visa sponsorship availability, so you can browse analytics engineer roles in New York without sorting through positions that don't sponsor. The platform surfaces employers who have a documented history of filing for work visas in technical roles, which is particularly useful in New York where many postings don't explicitly state sponsorship status upfront.
Are there any New York-specific considerations for analytics engineers seeking visa sponsorship?
New York's prevailing wage requirements for H-1B roles are set at competitive levels given the cost of living, particularly for Manhattan-based positions, meaning employers must meet Department of Labor wage determinations before filing. New York also has a strong university pipeline, with graduates from Columbia, NYU, and Cornell Tech frequently entering the local analytics engineering market and seeking OPT-to-H-1B transitions with established New York employers.
What is the prevailing wage for sponsored analytics engineer jobs in New York?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.