Senior Level Architect Jobs
Senior level architect jobs place experienced professionals in charge of design direction, project outcomes, and the technical teams and processes that bring complex builds to life. Openings are a mix of on-site, remote, and hybrid roles across Technology & Software, Consulting & Professional Services, and Electronics & Hardware, with employers like Capgemini, Wipro, and Dell Technologies hiring at this level now.
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Showing 4 of 232+ Senior Level Architect jobs
Title: Sr Data Architect
Location: Holmdel, NJ (Hybrid)
Duration: 12+ Months
Visa Status: USC/GC/H4 EAD
Tax Term: W2
Mandatory skills: Investment domain, Databricks, Strong Python
Job Description:
The Senior Data Engineer will design, develop, and support scalable enterprise data solutions for Clients Investments organization. The role will build high-performing data pipelines and data platforms that deliver reliable investment data for reporting, analytics, and downstream business processes. The engineer will work with investment data across multiple asset classes, including fixed income, equities, commercial mortgage loans, private assets, and derivatives. The role requires strong expertise in modern data engineering, investment data, data quality, and production support, along with the ability to collaborate with product owners, architects, business stakeholders, and engineering teams in an Agile environment.
Key Responsibilities
Requirements Elicitation & Analysis
- Understand product vision, business requirements, data models, and platform objectives.
- Collaborate with Product Owners, SMEs, and Architects to define technical requirements and data mapping specifications.
- Translate business rules into detailed low-level technical designs (LLD).
Project Planning, Tracking & Reporting
- Estimate efforts for design, build, and test activities for assigned modules.
- Provide input to sprint planning, schedule creation, and dependency identification.
- Track and report progress, highlighting risks, blockers, and scope variations.
- Ensure timely, high-quality delivery of assigned work items.
Design
- Develop low-level designs for ingestion, transformation, and delivery pipelines.
- Evaluate alternate solutions and recommend optimal technical approaches.
- Present LLDs in formal reviews and incorporate feedback.
Development & Support
- Build scalable, high-performing ETL/ELT pipelines using approved tools and coding standards.
- Implement complex investment data transformations involving trades, positions, valuations, pricing, and security master data.
- Adhere to version control, code management, and DevOps practices.
- Perform peer reviews.
Testing, Debugging & Validation
- Participate in test case walkthroughs and validate coverage for functional and non-functional requirements.
- Conduct impact analysis for enhancements and defects.
- Lead debugging efforts for complex data or performance issues; escalate risks as needed.
Documentation
- Create and maintain technical documentation, including data mappings, transformation logic, unit test results, and operational runbooks.
- Ensure documentation meets SDLC and audit requirements.
Process, Compliance & Governance
- Follow established SDLC, data governance, security, and audit frameworks.
- Support internal and external audit requests related to the data platform.
- Ensure engineering practices adhere to corporate standards.
Qualifications & Required Skills
Must Have Skills
Data Engineering and SQL
- Strong experience in SQL development, complex query design, performance tuning, and large-scale data processing.
- Proven experience designing scalable data pipelines and database solutions that support high-volume integration, reporting, and analytics workloads.
- Strong knowledge of relational, dimensional, and analytical data modeling.
- Experience implementing data quality, validation, reconciliation, exception handling, auditability, and restart and recovery capabilities.
Databricks, Spark, and Python
- Strong hands-on experience with Databricks, Apache Spark, PySpark, and distributed data-processing frameworks.
- Experience developing common frameworks for ingestion, transformation, validation, logging, monitoring, and automation.
- Knowledge of Lakehouse architecture and cloud-based data engineering patterns.
- Experience optimizing Spark jobs, partitioning strategies, storage formats, and pipeline performance.
- Strong Python development skills with experience building reusable, modular, testable, and production-ready data engineering solutions.
ETL/ELT and Orchestration
- Experience designing and implementing enterprise-scale ETL/ELT pipelines using Informatica or equivalent data integration platforms.
- Experience building workflows with robust scheduling, dependency management, error handling, recovery, and operational monitoring.
- Experience with enterprise scheduling and orchestration tools such as Control-M or comparable platforms.
Investment Data
- Strong understanding of investment data, including securities, trades, transactions, positions, holdings, cash, pricing, valuations, and benchmarks.
- Familiarity with multiple asset classes, including fixed income, equities, commercial mortgage loans, private equity, private placements, and derivative products.
- Understanding of security master, reference data, market data, investment accounting, and downstream reporting processes.
- Ability to translate investment business rules into technical mappings, transformation logic, and validation controls.
DevOps and Delivery
- Experience with Git-based version control and CI/CD platforms such as Azure DevOps, Jenkins, or GitHub Actions.
- Experience with automated testing, deployment, release management, and environment configuration.
- Experience working in Agile delivery environments using Scrum, Kanban, or SAFe practices.
- Strong collaboration skills with product owners, architects, business stakeholders, quality assurance teams, and engineers.
Nice to have
- Experience working with investment accounting and investment data management platforms such as Eagle PACE, Neoxam.
- Experience designing or integrating generative AI solutions into enterprise data platforms and business workflows.
- Understanding of large language models, prompt engineering, embeddings, semantic search, vector databases, and Retrieval-Augmented Generation architectures.
- Experience using AI-assisted engineering tools such as GitHub Copilot, Microsoft Copilot, or Databricks Assistant to improve development, testing, documentation, or operational productivity.
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- Typically 8-10 years of experience in data engineering roles.
Behavioral Competencies
- Analytical and detail-oriented with strong problem-solving skills.
- Highly collaborative and comfortable working with cross-functional teams.
- Effective in communicating technical concepts to business stakeholders.
- Self-driven with an ownership mindset and focus on delivery excellence.
- Adaptable and able to thrive in a fast-paced, Agile environment.
Additional Information
Occasional evening or weekend work may be required during critical releases or production support windows.
Title: Sr Data Architect
Location: Holmdel, NJ (Hybrid)
Duration: 12+ Months
Visa Status: USC/GC/H4 EAD
Tax Term: W2
Mandatory skills: Investment domain, Databricks, Strong Python
Job Description:
The Senior Data Engineer will design, develop, and support scalable enterprise data solutions for Clients Investments organization. The role will build high-performing data pipelines and data platforms that deliver reliable investment data for reporting, analytics, and downstream business processes. The engineer will work with investment data across multiple asset classes, including fixed income, equities, commercial mortgage loans, private assets, and derivatives. The role requires strong expertise in modern data engineering, investment data, data quality, and production support, along with the ability to collaborate with product owners, architects, business stakeholders, and engineering teams in an Agile environment.
Key Responsibilities
Requirements Elicitation & Analysis
- Understand product vision, business requirements, data models, and platform objectives.
- Collaborate with Product Owners, SMEs, and Architects to define technical requirements and data mapping specifications.
- Translate business rules into detailed low-level technical designs (LLD).
Project Planning, Tracking & Reporting
- Estimate efforts for design, build, and test activities for assigned modules.
- Provide input to sprint planning, schedule creation, and dependency identification.
- Track and report progress, highlighting risks, blockers, and scope variations.
- Ensure timely, high-quality delivery of assigned work items.
Design
- Develop low-level designs for ingestion, transformation, and delivery pipelines.
- Evaluate alternate solutions and recommend optimal technical approaches.
- Present LLDs in formal reviews and incorporate feedback.
Development & Support
- Build scalable, high-performing ETL/ELT pipelines using approved tools and coding standards.
- Implement complex investment data transformations involving trades, positions, valuations, pricing, and security master data.
- Adhere to version control, code management, and DevOps practices.
- Perform peer reviews.
Testing, Debugging & Validation
- Participate in test case walkthroughs and validate coverage for functional and non-functional requirements.
- Conduct impact analysis for enhancements and defects.
- Lead debugging efforts for complex data or performance issues; escalate risks as needed.
Documentation
- Create and maintain technical documentation, including data mappings, transformation logic, unit test results, and operational runbooks.
- Ensure documentation meets SDLC and audit requirements.
Process, Compliance & Governance
- Follow established SDLC, data governance, security, and audit frameworks.
- Support internal and external audit requests related to the data platform.
- Ensure engineering practices adhere to corporate standards.
Qualifications & Required Skills
Must Have Skills
Data Engineering and SQL
- Strong experience in SQL development, complex query design, performance tuning, and large-scale data processing.
- Proven experience designing scalable data pipelines and database solutions that support high-volume integration, reporting, and analytics workloads.
- Strong knowledge of relational, dimensional, and analytical data modeling.
- Experience implementing data quality, validation, reconciliation, exception handling, auditability, and restart and recovery capabilities.
Databricks, Spark, and Python
- Strong hands-on experience with Databricks, Apache Spark, PySpark, and distributed data-processing frameworks.
- Experience developing common frameworks for ingestion, transformation, validation, logging, monitoring, and automation.
- Knowledge of Lakehouse architecture and cloud-based data engineering patterns.
- Experience optimizing Spark jobs, partitioning strategies, storage formats, and pipeline performance.
- Strong Python development skills with experience building reusable, modular, testable, and production-ready data engineering solutions.
ETL/ELT and Orchestration
- Experience designing and implementing enterprise-scale ETL/ELT pipelines using Informatica or equivalent data integration platforms.
- Experience building workflows with robust scheduling, dependency management, error handling, recovery, and operational monitoring.
- Experience with enterprise scheduling and orchestration tools such as Control-M or comparable platforms.
Investment Data
- Strong understanding of investment data, including securities, trades, transactions, positions, holdings, cash, pricing, valuations, and benchmarks.
- Familiarity with multiple asset classes, including fixed income, equities, commercial mortgage loans, private equity, private placements, and derivative products.
- Understanding of security master, reference data, market data, investment accounting, and downstream reporting processes.
- Ability to translate investment business rules into technical mappings, transformation logic, and validation controls.
DevOps and Delivery
- Experience with Git-based version control and CI/CD platforms such as Azure DevOps, Jenkins, or GitHub Actions.
- Experience with automated testing, deployment, release management, and environment configuration.
- Experience working in Agile delivery environments using Scrum, Kanban, or SAFe practices.
- Strong collaboration skills with product owners, architects, business stakeholders, quality assurance teams, and engineers.
Nice to have
- Experience working with investment accounting and investment data management platforms such as Eagle PACE, Neoxam.
- Experience designing or integrating generative AI solutions into enterprise data platforms and business workflows.
- Understanding of large language models, prompt engineering, embeddings, semantic search, vector databases, and Retrieval-Augmented Generation architectures.
- Experience using AI-assisted engineering tools such as GitHub Copilot, Microsoft Copilot, or Databricks Assistant to improve development, testing, documentation, or operational productivity.
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- Typically 8-10 years of experience in data engineering roles.
Behavioral Competencies
- Analytical and detail-oriented with strong problem-solving skills.
- Highly collaborative and comfortable working with cross-functional teams.
- Effective in communicating technical concepts to business stakeholders.
- Self-driven with an ownership mindset and focus on delivery excellence.
- Adaptable and able to thrive in a fast-paced, Agile environment.
Additional Information
Occasional evening or weekend work may be required during critical releases or production support windows.
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Find Senior Level Architect JobsSenior Level Architect Job Market
Who's Hiring
- Capgemini12
- Wipro8
- Dell Technologies7
- Lorven Technologies7
- HH Global5
Top Industries Hiring
- Technology & Software105
- Consulting & Professional Services36
- Electronics & Hardware13
- Construction & Real Estate8
- Marketing & Advertising6
Senior Level Architect Jobs: Frequently Asked Questions
How do I get a senior level architect job?
Senior level architect roles go to candidates who demonstrate ownership of full project cycles, not just execution of tasks. Hiring managers look for a portfolio showing complex, high-stakes work, evidence of leading project teams or junior staff, and fluency in client-facing decisions. Licensure, breadth across project types, and a reputation for delivering on scope and schedule all give candidates a clear edge at this stage.
Which companies hire senior level architects?
Companies hiring senior level architects right now include Capgemini, Wipro, and Dell Technologies, based on current listings on Migrate Mate as of November 2026. Hiring at this level comes from a mix of large architecture and engineering firms, real estate developers, construction companies, and institutions managing significant capital projects.
Are there remote senior level architect jobs?
Yes, though most senior architect roles involve some on-site presence given the nature of the work. About 42% of senior level architect openings are remote or hybrid as of November 2026, with hybrid arrangements most common for roles focused on design, documentation, and client coordination rather than active construction administration.
What makes an architect role senior level?
Senior architect roles are defined by ownership and scope rather than task completion. At this level, you are responsible for project design direction, quality outcomes, and client relationships, and you are expected to mentor and guide less experienced team members. The work involves exercising independent professional judgment, managing competing priorities across a project, and being accountable for decisions that affect budget, schedule, and built quality.
Which industries hire the most senior level architects?
Senior level architect roles concentrate in Technology & Software, Consulting & Professional Services, and Electronics & Hardware, based on current listings on Migrate Mate as of November 2026. These sectors drive demand because they manage large, technically complex building programs where experienced design leadership directly affects project performance and risk.