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Senior 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 postingDesign and optimize batch and near-real-time ETL/ELT pipelines in Python, PySpark and SQL
Build and manage Airflow or cloud-native workflow orchestrations and automate ingestion
Design warehouse and lakehouse layers with partitioning, indexing and SCD Type 2 patterns
Establish data quality checks and validation frameworks using dbt, Soda or PySpark tests
Lead code reviews and configure CI/CD pipelines with Docker containers
From the employer’s posting
Design, build, and optimize scalable, reliable batch and near-real-time ETL/ELT pipelines using Python, PySpark, SQL
Develop and manage complex workflow orchestrations (using Apache Airflow or cloud native schedulers) and automate ingestion routines
Design and implement modern data warehouse/lakehouse layers
Establish rigorous data quality checks and validation frameworks utilizing tools like dbt (data build tool), Soda, or customized PySpark testing suites
Lead code reviews, enforce engineering standards, and configure robust CI/CD pipelines
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 30, 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.
- Python
- SQL
- AWS
- Azure
- ClickHouse
- dbt
- Delta Lake
- Docker
- GCP
- Kafka
- Kubernetes
- PostgreSQL
- Redshift
- Snowflake
- Spark
- SQL Server
- Airflow
- PySpark
Where the posting names them
We are seeking a highly skilled, solution-oriented Senior Data Integration Engineer with deep expertise in modern data engineering, cloud-native architectures, and robust pipeline development. In this role, you will be a senior technical driver in creating, optimizing, and modernizing advanced data integration systems. You will collaborate closely with product, engineering, and architecture teams to bridge raw source environments and analytical databases. If you excel in cloud ecosystems, thrive on automating complex data workflows, and value precision, reliability, and data quality above all, we encourage you to apply! Responsibilities Design, build, and optimize scalable, reliable batch and near-real-time ETL/ELT pipelines using Python, PySpark, SQL, and modern cloud integration engines Develop and manage complex workflow orchestrations (using Apache Airflow or cloud native schedulers) and automate ingestion routines to minimize manual operations Design and implement modern data warehouse/lakehouse layers (using Snowflake, ClickHouse, Azure Synapse, or Redshift), establishing optimal partitioning, indexing, and Slowly Changing Dimension (SCD Type 2) patterns Establish rigorous data quality checks and validation frameworks utilizing tools like dbt (data build tool), Soda, or customized PySpark testing suites Work closely with product owners, business analysts, and systems architects to define data requirements, analyze technical constraints, design Source-to-Target Mappings (STTM), and make critical architectural decisions Maintain a clean, modular code repository. Lead code reviews, enforce engineering standards, and configure robust CI/CD pipelines (Azure DevOps, GitLab CI, or GitHub Actions) with Docker containers Deliver comprehensive, clear technical specs, metadata lineage documentation, architectural diagrams, and data dictionaries Requirements 5+ years of hands-on experience in data engineering, data warehousing, database design, and end-to-end data integration Advanced knowledge of Cloud Integration tools such as Azure Data Factory (ADF), AWS Glue, or GCP Dataflow Proficiency in workflow orchestrators like Apache Airflow and exposure to CDC (Change Data Capture) or real-time streaming tools (e.g., Kafka, Debezium) Strong production-level coding skills in SQL (advanced optimization/stored procedures), Python, and PySpark / Apache Spark Experience working with high-performance databases and cloud-native systems (e.g., Snowflake, ClickHouse, PostgreSQL, MS SQL Server, or Azure Synapse) Master-level understanding of data modeling practices (OLAP, OLTP, Star/Snowflake schemas, Delta Lake/Lakehouse patterns, and Data staging processes) Hands-on experience with version control (Git) and building automated deployment pipelines (CI/CD) for data products Proven ability to articulate complex technical ideas clearly to both business stakeholders and developers Fluency in English (Upper-Intermediate level or higher) Nice to have Deep knowledge of dbt (data build tool) and schema validation practices Experience using Docker or Kubernetes to package and deploy data applications Experience building lightweight, serverless ingestion services (e.g., using AWS Lambda / Azure Functions and RESTful APIs)
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 23 other Senior Data Integration Engineer jobs there since November 2026. All 23 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 a highly skilled, solution-oriented Senior Data Integration Engineer with deep expertise in modern data engineering, cloud-native architectures, and robust pipeline development. In this role, you will be a senior technical driver in creating, optimizing, and modernizing advanced data integration systems. You will collaborate closely with product, engineering, and architecture teams to bridge raw source environments and analytical databases. If you excel in cloud ecosystems, thrive on automating complex data workflows, and value precision, reliability, and data quality above all, we encourage you to apply! Responsibilities Design, build, and optimize scalable, reliable batch and near-real-time ETL/ELT pipelines using Python, PySpark, SQL, and modern cloud integration engines Develop and manage complex workflow orchestrations (using Apache Airflow or cloud native schedulers) and automate ingestion routines to minimize manual operations Design and implement modern data warehouse/lakehouse layers (using Snowflake, ClickHouse, Azure Synapse, or Redshift), establishing optimal partitioning, indexing, and Slowly Changing Dimension (SCD Type 2) patterns Establish rigorous data quality checks and validation frameworks utilizing tools like dbt (data build tool), Soda, or customized PySpark testing suites Work closely with product owners, business analysts, and systems architects to define data requirements, analyze technical constraints, design Source-to-Target Mappings (STTM), and make critical architectural decisions Maintain a clean, modular code repository. Lead code reviews, enforce engineering standards, and configure robust CI/CD pipelines (Azure DevOps, GitLab CI, or GitHub Actions) with Docker containers Deliver comprehensive, clear technical specs, metadata lineage documentation, architectural diagrams, and data dictionaries Requirements 5+ years of hands-on experience in data engineering, data warehousing, database design, and end-to-end data integration Advanced knowledge of Cloud Integration tools such as Azure Data Factory (ADF), AWS Glue, or GCP Dataflow Proficiency in workflow orchestrators like Apache Airflow and exposure to CDC (Change Data Capture) or real-time streaming tools (e.g., Kafka, Debezium) Strong production-level coding skills in SQL (advanced optimization/stored procedures), Python, and PySpark / Apache Spark Experience working with high-performance databases and cloud-native systems (e.g., Snowflake, ClickHouse, PostgreSQL, MS SQL Server, or Azure Synapse) Master-level understanding of data modeling practices (OLAP, OLTP, Star/Snowflake schemas, Delta Lake/Lakehouse patterns, and Data staging processes) Hands-on experience with version control (Git) and building automated deployment pipelines (CI/CD) for data products Proven ability to articulate complex technical ideas clearly to both business stakeholders and developers Fluency in English (Upper-Intermediate level or higher) Nice to have Deep knowledge of dbt (data build tool) and schema validation practices Experience using Docker or Kubernetes to package and deploy data applications Experience building lightweight, serverless ingestion services (e.g., using AWS Lambda / Azure Functions and RESTful APIs)
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