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# Lead Data Engineer with Java, Python, Spark & AWS - Onsite - W2 only

- **Company:** MSYS Inc.
- **Location:** Columbus, OH, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Apache HTTP Server, Automation of Tests, Continuous Integration, Data Architecture, Data Governance, Extract Transform Load (ETL), Distributed Computing Environment, Python (Programming Language), Performance Tuning, Software Engineering, Parquet, Data Ingestion, Snowflake, Apache Spark, Generative AI, Agentic-AI, Data Lakes, Avro, Apache Kafka, Spark Streaming, Restful APIs, Data Pipelines, Control M
- **Published:** November 14, 2026
- **Apply:** https://www.dice.com/job-detail/cd6327e1-abe4-4b33-a2f9-112a32a8897b

## About the Role

* 10+ years of software engineering experience.
 * Strong hands-on Java development.
 * Python proficiency.
 * ETL/ELT pipeline development.
 * Apache Spark and distributed processing.
 * AWS cloud experience.
 * Parquet, Avro, and data lake/lakehouse architecture.
 * Snowflake or equivalent cloud data warehouse experience.
 * AI-assisted development tools and responsible AI practices.

Preferred Skills

 * Airflow, Dagster, or Control-M.
 * Kafka and Spark Structured Streaming.
 * Delta Lake, Apache Iceberg, or Hudi.
 * Data governance and data quality.
 * Financial services or regulated industry experience.
 * Agentic AI and Generative AI exposure.

## Description

Seeking an experienced Lead Data Engineer with strong expertise in Java, Python, AWS, ETL/ELT pipelines, and Apache Spark., * Lead architecture and development of large-scale ETL/ELT data pipelines.

 * Design data ingestion, transformation, validation, and reconciliation workflows.
 * Develop distributed data processing solutions using Apache Spark.
 * Design and implement data lake and lakehouse architectures.
 * Optimize Spark workloads, including joins, caching, partitioning, and performance tuning.
 * Develop Java-based services and REST APIs.
 * Implement automated testing, CI/CD, monitoring, and production support.
 * Drive engineering best practices and mentor development teams.
 * Promote enterprise-approved AI-assisted engineering practices.