Lead Data Engineer – AWS ETL & Data Modernization

Simplify Recruiting

United States

On-site

USD 150,000 - 230,000

Full time

3 days ago

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Job summary

Simplify Recruiting in the United States seeks a Lead Data Engineer to own the data platform direction, guiding AWS-native pipelines and legacy SQL Server/SSIS ETL modernization. You will mentor a team and collaborate with analytics, DevOps, and BI stakeholders.

You will define standards, drive performance improvements, and ensure data quality, lineage, and reliability across deployments. This role blends hands-on engineering with leadership to deliver scalable data solutions.

Qualifications

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
10-12 years of data engineering/ETL with progression toward technical leadership.
Hands-on experience with AWS data services: S3, Redshift, Glue, Athena, Lambda, and Step Functions.
Strong SQL Server experience including T-SQL development, query optimization, and performance tuning.
Proven ability to build and maintain SSIS ETL packages (control flow, data flow, error handling, deployment).
Strong Python skills for scripting and Glue/PySpark-based ETL development.
Deep expertise in dimensional data modeling (star schema) for analytics.
Experience leading data migration programs using AWS DMS or similar tooling.
Strong governance, data quality, lineage practices and standards enforcement.
Excellent debugging, performance tuning, and production-support skills.
Experience leading technical projects or data-engineering teams.
Excellent written and verbal communication for cross-functional collaboration.

Responsibilities

Lead ETL/ELT pipelines using AWS Glue, Lambda, and Step Functions across AWS data platforms (S3, Redshift, Athena).
Own the migration and modernization of legacy SQL Server/SSIS workloads to AWS-native pipelines.
Maintain and optimize existing SQL Server/SSIS ETL processes and error handling.
Define coding standards, design patterns, and best practices for the data engineering team.
Write and optimize complex T-SQL queries, stored procedures, and views for reporting.
Architect and govern dimensional data models to support analytics and reporting.
Lead data migration efforts using AWS DMS from on-premises to AWS.
Enforce data quality, validation, monitoring, and lineage standards across pipelines.
Drive performance improvements, reliability, and cost efficiency across pipelines.
Mentor data engineers and conduct meaningful code reviews.
Maintain thorough documentation of data flows and pipeline architectures.