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Lead Data Integration Engineer
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: TELECOMMUTE
From the employer’s posting- Employment
- Not stated
What you’ll work on
Full postingLead and mentor a team of data integration and ETL engineers
Architect end-to-end data pipeline solutions on serverless or hybrid setups
Set up orchestration and scheduling with Apache Airflow or cloud-native tools
Run code reviews and testing frameworks for SQL, Python and Scala code
Present advanced technical architectures to C-level client executives
From the employer’s posting
Guide a team of data integration and ETL engineers, fostering a collaborative, innovation-driven culture
Architect end-to-end data pipeline solutions using serverless frameworks or hybrid setups
Establish robust pipeline scheduling and workflow orchestration using tools like Apache Airflow (Astronomer) or cloud-native schedulers
Conduct regular code reviews and set up testing frameworks to ensure compliant, highly performant, and secure database code (SQL, Python, Scala)
Confidently present advanced technical architectures to C-level client executives
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 1, 20262 days ago
- Recorded sightings
- 4
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Oct 27, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EPAM Systems is hiring
656 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Bash
- C
- Python
- Scala
- SQL
- AWS
- Azure
- BigQuery
- GCP
- Redshift
- S3
- Snowflake
- Airflow
- pandas
- PySpark
Where the posting names them
We are seeking an experienced and visionary Lead Data Integration Engineer to join our growing data solutions team. In this pivotal technical leadership role, you will act as a Tech Lead / Team Lead responsible for architecting, designing, and maintaining complex, enterprise-grade data pipelines. You will lead a high-performing engineering team (5+ engineers), shaping cloud-native and hybrid data solution architectures while establishing best practices. Leveraging your extensive expertise in cloud infrastructure, database modeling, orchestration, and data security, you will transform raw data environments into robust, high-performance, and scalable analytical assets. Responsibilities Guide a team of data integration and ETL engineers, fostering a collaborative, innovation-driven culture. Manage sprint planning, resource allocation, and risk management Conduct regular code reviews and set up testing frameworks to ensure compliant, highly performant, and secure database code (SQL, Python, Scala) Act as a role model, championing modern software development practices (CI/CD, version control via Git, modular code design, automated alerting, and monitoring) Architect end-to-end data pipeline solutions using serverless frameworks or hybrid setups Establish robust pipeline scheduling and workflow orchestration using tools like Apache Airflow (Astronomer) or cloud-native schedulers to achieve high pipeline reliability (>99.5%) and minimize troubleshooting times Design, model, and maintain scalable corporate data systems (OLAP, OLTP, Star/Snowflake schemas, Data Lakes, Lakehouses, and Data Meshes) Implement modern ingestion patterns, CDC methods, micro-batching, delta extracts, and routine partition maintenance Collaborate with product owners, enterprise architects, and business stakeholders to translate complex requirements into detailed technical specifications and data-flow diagrams Confidently present advanced technical architectures to C-level client executives, showcasing ROI, architectural benefits, and performance gains Requirements 5+ years of proven experience in data management, database design, migration, storage, and advanced data modeling Demonstrated experience leading, mentoring, and developing teams of data engineers (experience managing teams of 4–7 engineers is highly preferred) Deep hands-on experience with major ETL tools such as Talend, AWS Glue, Azure Data Factory, GCP Dataflow, or Apache NiFi Strong working experience with orchestration and scheduling engines, particularly Apache Airflow (Astronomer) Strong production coding skills in SQL, Python (PySpark, Pandas), Scala, SparkSQL, or Bash Extensive knowledge of at least one major cloud platform (AWS, Azure, GCP) with emphasis on big data warehousing engines (such as Redshift, Snowflake, Google BigQuery, or Azure Synapse) Proven understanding of cloud security structures (identity and access management, data masking, and RLS) and compliance standards (GDPR, HIPAA, PI) Ability to write high-quality technical design specifications, mapping documentation, and data lineage reports Excellent verbal and written English communication (B2+/C1 level), capable of directly managing international clients and conducting presentations Nice to have Experience architecting fully serverless cloud data pipelines (e.g., combining AWS S3, Athena, and AWS Lambda) Exposure to integrating machine learning models, computer vision, or generative AI workflows into data pipeline ingestion processes Professional certifications in Cloud Architecture (AWS, Azure, GCP), Talend, or Airflow
Your next step
Complete your application on careers.epam.com. The employer’s form will show what is required.
Already applied? Track this application
Has EPAM Systems posted this job before?
Yes. We’ve followed 8 other Lead Data Integration Engineer jobs there since November 2026. All 8 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
We are seeking an experienced and visionary Lead Data Integration Engineer to join our growing data solutions team. In this pivotal technical leadership role, you will act as a Tech Lead / Team Lead responsible for architecting, designing, and maintaining complex, enterprise-grade data pipelines. You will lead a high-performing engineering team (5+ engineers), shaping cloud-native and hybrid data solution architectures while establishing best practices. Leveraging your extensive expertise in cloud infrastructure, database modeling, orchestration, and data security, you will transform raw data environments into robust, high-performance, and scalable analytical assets. Responsibilities Guide a team of data integration and ETL engineers, fostering a collaborative, innovation-driven culture. Manage sprint planning, resource allocation, and risk management Conduct regular code reviews and set up testing frameworks to ensure compliant, highly performant, and secure database code (SQL, Python, Scala) Act as a role model, championing modern software development practices (CI/CD, version control via Git, modular code design, automated alerting, and monitoring) Architect end-to-end data pipeline solutions using serverless frameworks or hybrid setups Establish robust pipeline scheduling and workflow orchestration using tools like Apache Airflow (Astronomer) or cloud-native schedulers to achieve high pipeline reliability (>99.5%) and minimize troubleshooting times Design, model, and maintain scalable corporate data systems (OLAP, OLTP, Star/Snowflake schemas, Data Lakes, Lakehouses, and Data Meshes) Implement modern ingestion patterns, CDC methods, micro-batching, delta extracts, and routine partition maintenance Collaborate with product owners, enterprise architects, and business stakeholders to translate complex requirements into detailed technical specifications and data-flow diagrams Confidently present advanced technical architectures to C-level client executives, showcasing ROI, architectural benefits, and performance gains Requirements 5+ years of proven experience in data management, database design, migration, storage, and advanced data modeling Demonstrated experience leading, mentoring, and developing teams of data engineers (experience managing teams of 4–7 engineers is highly preferred) Deep hands-on experience with major ETL tools such as Talend, AWS Glue, Azure Data Factory, GCP Dataflow, or Apache NiFi Strong working experience with orchestration and scheduling engines, particularly Apache Airflow (Astronomer) Strong production coding skills in SQL, Python (PySpark, Pandas), Scala, SparkSQL, or Bash Extensive knowledge of at least one major cloud platform (AWS, Azure, GCP) with emphasis on big data warehousing engines (such as Redshift, Snowflake, Google BigQuery, or Azure Synapse) Proven understanding of cloud security structures (identity and access management, data masking, and RLS) and compliance standards (GDPR, HIPAA, PI) Ability to write high-quality technical design specifications, mapping documentation, and data lineage reports Excellent verbal and written English communication (B2+/C1 level), capable of directly managing international clients and conducting presentations Nice to have Experience architecting fully serverless cloud data pipelines (e.g., combining AWS S3, Athena, and AWS Lambda) Exposure to integrating machine learning models, computer vision, or generative AI workflows into data pipeline ingestion processes Professional certifications in Cloud Architecture (AWS, Azure, GCP), Talend, or Airflow
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