Senior Data Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Senior Data Engineer Vilnius What success looks like in this role: Design, build, and maintain scalable ETL/ELT data pipelines. Develop and manage data models, data lakes, and data warehouses. Ensure data reliability, integrity, and performance across systems. Implement best practices for data governance, security, and compliance through strong architecture and design principles. Optimize data processing using distributed frameworks such as Spark. Work with cloud platforms such as Azure, Databricks, and to a smaller extent AWS or GCP to build scalable data infrastructure. Collaborate with developers, architects and stakeholders to align business requirements and deliver clean and accessible datasets. Monitor, troubleshoot, and improve existing data workflows. Implement CI/CD pipelines and data testing frameworks for data workflows. Stay up-to-date with emerging technologies, tools, and best practices in both application development and data engineering. You will be successful in this role if you have: At least 5+ years of experience in data engineering or related roles Strong proficiency in programming languages commonly used in data processing and application development including Python(must have) and good understanding of Scala or Java Hands-on experience with both SQL (must have) and NoSQL databases, ensuring efficient data modeling, storage and transformation. Hands-on experience with Spark as main distributed data processing frameworks Experience with data warehouses (Snowflake, Redshift, BigQuery) and data lake technologies (S3, Delta Lake, Iceberg, etc.) Experience building pipelines using tools such as Airflow, Apache NiFi, or Prefect Familiarity with cloud platforms especially Azure (preferred against AWS & GCP) and deploying scalable applications Experience with Databricks is

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