Data Operations Engineer Jobs
Data Operations Engineer jobs are open across fintech, healthtech, logistics, and enterprise SaaS, from entry-level to staff and principal, with specializations in data pipeline architecture, platform reliability, and observability engineering. Find a role that fits below and apply directly.
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Showing 5 of 181+ Data Operations Engineer jobs
Data Operations Engineer
W2 contract, min of 6 months
HYBRID out of Atlanta, GA; Dallas, TX; Houston, TX; Auburn Hills, MI
Target W2 $36/hr
Reporting to the Director Enterprise Data Management, the Engineer, Data Operations designs, develops, and operates secure, scalable, and high performing data and infrastructure solutions supporting a nationwide auto finance organization. This role serves as a technical contributor within the infrastructure team, driving data pipeline development, ETL optimization, and warehouse modernization across SQL Server, Azure Data Factory, and Microsoft Fabric, as well as the enterprise servers, storage, and software-defined cloud and network environments that underpin them. The Engineer, Data Operations partners with analytics, application, security, and operations teams to ensure data and infrastructure solutions align with enterprise architecture, regulatory requirements, and business objectives.
Essential Duties And Responsibilities
- Design, Develop, and Implement Data and ETL Solutions & Integrations – Assist in the design, development, and implementation of data solutions and integrations to optimize performance, scalability, and reliability.
- Monitor, Troubleshoot, and Resolve Problems – Implement and support effective monitoring to proactively identify and resolve issues, conducting effective root-cause analysis to address underlying problems and prevent recurrence.
- Provide Support & Optimization – Offer expertise in diagnosing and resolving issues by implementing the necessary corrective actions and/or fixes, ensuring the operational efficiency, reliability, and scalability of data systems and ETL processes.
- Document Technical Processes & Solutions – Maintain documentation for process and system configurations, engineering designs, and troubleshooting procedures to support knowledge sharing and ensure continuity of operations.
- Collaborate on Cross-Functional Projects – Work with team members and other departments to support business and technology initiatives and integrations, drive innovation, and improve workflows.
- Support Initiatives, Timelines, Budgets, and Deliverables – Support initiatives, timelines, budgets, and deliverables as assigned to ensure successful outcomes, managing priorities and resources effectively to meet business objectives.
- Data Analytics & Performance Monitoring – Collect data to track key metrics (KPIs, SLAs, and SLOs) and identify areas of risk and/or improvement using standard methodologies to deliver value to business partners.
- Ensures Business Alignment – Work closely with Product and/or Process Engineering team members to implement technical solutions with business partners that align with business goals using standard methodologies.
- Develop solutions to retrieve and process vendor data feeds, applying ETL processes to ensure efficient ingestion into enterprise staging systems.
- Develop T-SQL code to transform staging data from multiple vendors into a cohesive, business aligned warehouse structure, applying logic to enhance clarity and usability.
- Develop solutions to extract and transform data from the warehouse, applying customer specific business logic to create and deliver tailored datasets for internal and external consumers.
- Monitor, analyze, and optimize long-running T-SQL processes, applying performance tuning techniques to improve efficiency and ensure timely completion.
- Develop and/or contribute to the Enterprise Data Dictionary.
- Additional duties may be added as business needs evolve.
Qualifications
- Bachelor’s degree in Computer Science, Information Technology, or a related field, or an equivalent combination of education and relevant professional experience.
- Minimum of five years of experience in SQL development and support, with strong SQL Server fundamentals.
- Solid understanding of ETL development with SSIS workflows.
- Experience with Azure SQL setup and configuration, and Azure Data Factory (pipelines, activities, datasets, linked services, data flows, and integration runtimes), and Microsoft Fabric.
- Strong understanding of database systems monitoring and support, software best practices, and development methodologies.
- Experience with FTP tools for file transfer.
- DBA experience across SQL Server, Azure SQL, Fabric, Snowflake, and Redis Cache.
- Problem-Solving & Critical Thinking – Ability to analyze, troubleshoot, and develop solutions for complex issues and challenges, including troubleshooting in a multi-tier environment, supported by strong analytical and diagnostic skills.
- Design & Architecture – Knowledge of designing scalable, efficient, and secure data and ETL solutions.
- Automation – Proficiency in common database technologies (on-prem and cloud) and ETL tools, along with scripting and automation languages/tools, including T-SQL, cloud data factory, and data pipelines.
- Business & System Analysis – Ability to translate business requirements into high-level designs and technical specifications as needed.
- Database Optimization – Ability to analyze query execution plans and optimize database code, perform database maintenance and indexing, and conduct code reviews.
- Documentation & Communication – Ability to document processes, requirements, and troubleshooting steps clearly using industry-standard methodologies and formats, with strong communication skills to collaborate effectively with technical and non-technical stakeholders.
- Collaboration & Cross-Functional Work – Experience working with teams across different disciplines to integrate and optimize solutions.
- Frameworks & Methodologies – Experience utilizing relevant frameworks and methodologies to standardize solutions across on-prem and cloud database environments.
- Expertise in SQL Server (on-prem and cloud), with familiarity in Microsoft Fabric and Azure Data Factory.
- Proficiency in designing and managing data ingestion pipelines, ETL/ELT processes, metadata management, and automation of data flows across hybrid environments.
- Experience in building, optimizing, and managing data pipelines using Azure Fabric, ADF, and other data integration tools for efficient data movement and transformation. Ability to support BI and analytics teams by providing well-structured data models and optimized datasets for tools such as Power BI and Tableau.
- Cloud database experience across Azure SQL, Fabric, Redis Cache, Cosmos DB, and Snowflake.
- File Transfer – Ability to use FTP tools to transfer files.
- Strong time-management and problem-solving skills, with the ability to work with minimal supervision as a critical thinker and team player.
- Must have reliable transportation and live within a commutable distance to one of the following cities: Atlanta, GA; Dallas, TX; Houston, TX; Auburn Hills, MI.
PREFERRED QUALIFICATIONS
- Prior experience in financial services, auto lending, or other highly regulated industries strongly preferred.
- Master’s degree in a STEM discipline or Business Administration is a plus.
- Experience with Cosmos DB is a plus.
- One or more relevant certifications are preferred.
Data Operations Engineer
W2 contract, min of 6 months
HYBRID out of Atlanta, GA; Dallas, TX; Houston, TX; Auburn Hills, MI
Target W2 $36/hr
Reporting to the Director Enterprise Data Management, the Engineer, Data Operations designs, develops, and operates secure, scalable, and high performing data and infrastructure solutions supporting a nationwide auto finance organization. This role serves as a technical contributor within the infrastructure team, driving data pipeline development, ETL optimization, and warehouse modernization across SQL Server, Azure Data Factory, and Microsoft Fabric, as well as the enterprise servers, storage, and software-defined cloud and network environments that underpin them. The Engineer, Data Operations partners with analytics, application, security, and operations teams to ensure data and infrastructure solutions align with enterprise architecture, regulatory requirements, and business objectives.
Essential Duties And Responsibilities
- Design, Develop, and Implement Data and ETL Solutions & Integrations – Assist in the design, development, and implementation of data solutions and integrations to optimize performance, scalability, and reliability.
- Monitor, Troubleshoot, and Resolve Problems – Implement and support effective monitoring to proactively identify and resolve issues, conducting effective root-cause analysis to address underlying problems and prevent recurrence.
- Provide Support & Optimization – Offer expertise in diagnosing and resolving issues by implementing the necessary corrective actions and/or fixes, ensuring the operational efficiency, reliability, and scalability of data systems and ETL processes.
- Document Technical Processes & Solutions – Maintain documentation for process and system configurations, engineering designs, and troubleshooting procedures to support knowledge sharing and ensure continuity of operations.
- Collaborate on Cross-Functional Projects – Work with team members and other departments to support business and technology initiatives and integrations, drive innovation, and improve workflows.
- Support Initiatives, Timelines, Budgets, and Deliverables – Support initiatives, timelines, budgets, and deliverables as assigned to ensure successful outcomes, managing priorities and resources effectively to meet business objectives.
- Data Analytics & Performance Monitoring – Collect data to track key metrics (KPIs, SLAs, and SLOs) and identify areas of risk and/or improvement using standard methodologies to deliver value to business partners.
- Ensures Business Alignment – Work closely with Product and/or Process Engineering team members to implement technical solutions with business partners that align with business goals using standard methodologies.
- Develop solutions to retrieve and process vendor data feeds, applying ETL processes to ensure efficient ingestion into enterprise staging systems.
- Develop T-SQL code to transform staging data from multiple vendors into a cohesive, business aligned warehouse structure, applying logic to enhance clarity and usability.
- Develop solutions to extract and transform data from the warehouse, applying customer specific business logic to create and deliver tailored datasets for internal and external consumers.
- Monitor, analyze, and optimize long-running T-SQL processes, applying performance tuning techniques to improve efficiency and ensure timely completion.
- Develop and/or contribute to the Enterprise Data Dictionary.
- Additional duties may be added as business needs evolve.
Qualifications
- Bachelor’s degree in Computer Science, Information Technology, or a related field, or an equivalent combination of education and relevant professional experience.
- Minimum of five years of experience in SQL development and support, with strong SQL Server fundamentals.
- Solid understanding of ETL development with SSIS workflows.
- Experience with Azure SQL setup and configuration, and Azure Data Factory (pipelines, activities, datasets, linked services, data flows, and integration runtimes), and Microsoft Fabric.
- Strong understanding of database systems monitoring and support, software best practices, and development methodologies.
- Experience with FTP tools for file transfer.
- DBA experience across SQL Server, Azure SQL, Fabric, Snowflake, and Redis Cache.
- Problem-Solving & Critical Thinking – Ability to analyze, troubleshoot, and develop solutions for complex issues and challenges, including troubleshooting in a multi-tier environment, supported by strong analytical and diagnostic skills.
- Design & Architecture – Knowledge of designing scalable, efficient, and secure data and ETL solutions.
- Automation – Proficiency in common database technologies (on-prem and cloud) and ETL tools, along with scripting and automation languages/tools, including T-SQL, cloud data factory, and data pipelines.
- Business & System Analysis – Ability to translate business requirements into high-level designs and technical specifications as needed.
- Database Optimization – Ability to analyze query execution plans and optimize database code, perform database maintenance and indexing, and conduct code reviews.
- Documentation & Communication – Ability to document processes, requirements, and troubleshooting steps clearly using industry-standard methodologies and formats, with strong communication skills to collaborate effectively with technical and non-technical stakeholders.
- Collaboration & Cross-Functional Work – Experience working with teams across different disciplines to integrate and optimize solutions.
- Frameworks & Methodologies – Experience utilizing relevant frameworks and methodologies to standardize solutions across on-prem and cloud database environments.
- Expertise in SQL Server (on-prem and cloud), with familiarity in Microsoft Fabric and Azure Data Factory.
- Proficiency in designing and managing data ingestion pipelines, ETL/ELT processes, metadata management, and automation of data flows across hybrid environments.
- Experience in building, optimizing, and managing data pipelines using Azure Fabric, ADF, and other data integration tools for efficient data movement and transformation. Ability to support BI and analytics teams by providing well-structured data models and optimized datasets for tools such as Power BI and Tableau.
- Cloud database experience across Azure SQL, Fabric, Redis Cache, Cosmos DB, and Snowflake.
- File Transfer – Ability to use FTP tools to transfer files.
- Strong time-management and problem-solving skills, with the ability to work with minimal supervision as a critical thinker and team player.
- Must have reliable transportation and live within a commutable distance to one of the following cities: Atlanta, GA; Dallas, TX; Houston, TX; Auburn Hills, MI.
PREFERRED QUALIFICATIONS
- Prior experience in financial services, auto lending, or other highly regulated industries strongly preferred.
- Master’s degree in a STEM discipline or Business Administration is a plus.
- Experience with Cosmos DB is a plus.
- One or more relevant certifications are preferred.
Data Operations Engineer Jobs by Experience Level
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Find Data Operations Engineer JobsData Operations Engineer Job Market
Who's Hiring
- Meta39
- Apple22
- Amazon22
- Lambda4
- ByteDance3
Top Industries Hiring
- Electronics & Hardware
- Technology & Software
- Food & Beverage
- Investment & Asset Management
- Consulting & Professional Services
What Employers Look For
The qualifications that appear most often in data operations engineer jobs.
- Proficiency with workflow orchestration tools such as Apache Airflow or Prefect
- Experience building and maintaining data pipelines using Python or SQL
- Hands-on work with cloud data warehouses like Snowflake, BigQuery, or Redshift
- Familiarity with containerization and infrastructure tools including Docker and Kubernetes
- Understanding of data quality monitoring, alerting, and observability practices
- Bachelor's degree in computer science, data engineering, or a related technical field
Tips for Your Data Operations Engineer Job Search
Tailor your resume to pipeline tooling
Hiring managers for data operations engineer roles scan for specific orchestration and transformation tools. Name the exact versions you've used, like Airflow 2.x or dbt Core, rather than listing broad categories like 'data pipeline tools' or 'ETL experience'.
Highlight on-call and incident ownership
Many data operations engineer postings list incident response and SLA ownership as requirements, not nice-to-haves. Quantify your impact by describing pipeline uptime you maintained or the scope of data systems you kept running during outages.
Apply early to roles that fit
Migrate Mate lists data operations engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings that match your data stack
Data operations engineer roles vary sharply by stack. A team running Spark on Kubernetes hires differently than one using Fivetran and Snowflake. Filter openings by the tools named in the description so you're not rewriting your resume for every application.
Prepare a system design answer for data reliability
Expect at least one interview round focused on designing a fault-tolerant pipeline or a monitoring system for data freshness. Practice talking through your design choices out loud, including tradeoffs between cost, latency, and observability, before your first screen.
Negotiate scope alongside compensation
Data operations engineer roles differ widely in whether you own the full data platform or only a subset of pipelines. During offer conversations, clarify ownership boundaries, on-call rotation frequency, and infrastructure access before finalizing your decision.
Data Operations Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data operations engineers?
The most active employers for data operations engineers right now are Meta, Apple, and Amazon, and the most openings are in Texas, California, and Virginia, based on current listings on Migrate Mate as of November 2026. Demand is spread across fintech, healthtech, and enterprise SaaS companies scaling their data platforms.
How many data operations engineer jobs are remote?
About 45% of data operations engineer openings are fully remote or hybrid as of November 2026, reflecting the infrastructure-heavy but cloud-native nature of the work. Roles focused on pipeline development and observability engineering tend to have the highest remote availability, while positions requiring close collaboration with on-site data centers or hardware are more likely to be on-site.
How do you become a data operations engineer?
Start by building proficiency in Python and SQL, then learn a workflow orchestration tool like Airflow. Get comfortable with at least one cloud data warehouse and understand how to monitor pipeline health. Work on projects that involve moving and transforming real data, document what you built, and apply to entry-level data engineering or analytics engineering roles that grow toward full data operations ownership.
Can you get hired as a data operations engineer with little experience?
Yes, employers do hire candidates with limited professional experience when they can demonstrate hands-on pipeline work. Build a portfolio project that ingests real data, transforms it, and surfaces a monitoring alert when something breaks. Roles titled junior data engineer or data analyst with an engineering focus are common entry points that lead directly into data operations engineering positions.
What does the data operations engineer interview process look like?
Most processes include an initial recruiter screen, a technical phone interview covering SQL and Python, and then a system design round where you walk through building or debugging a data pipeline. Some employers add a take-home exercise involving a broken pipeline or a data quality scenario. Final rounds typically involve a team panel and a conversation with an engineering manager about operational practices.
Where can I find and apply to data operations engineer jobs?
You can find and apply to data operations engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and stack, then apply directly to each listing from the page.
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Find roles that match your experience and apply in just a few clicks.
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