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# Lead Data Engineer, Advanced

- **Company:** BCforward
- **Location:** Columbus, OH, United States
- **Experience:** Expert
- **Salary:** $156,000 - $170,560
- **Contract:** Temporary to permanent
- **Skills:** Java (Programming Language), Multitier Architecture, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Apache HTTP Server, Automation of Tests, Big Data, Cloud Database, Software Quality, Continuous Integration, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Security, Serialization, Programming Tools, Distributed Computing Environment, Python (Programming Language), Metadata, Performance Tuning, Systems Development Life Cycle, Secure Coding, Software Engineering, Software Systems, Parquet, Data Storage Technologies, Snowflake, Apache Spark, Caching, Generative AI, Agentic-AI, Data Lakes, Solid Principles, Avro, Apache Kafka, Spark Streaming, Data Management, Restful APIs, Data Pipelines, Control M
- **Published:** November 12, 2026
- **Apply:** https://www.dice.com/job-detail/471e73d0-ae00-4b4b-ba69-5490d36d54c0

## About the Role

We are seeking a Lead Data Engineer to join our team. The ideal candidate will have strong experience in Java, Python, AWS, Apache Spark, and large-scale ETL/ELT data engineering and a proven ability to design and deliver high-quality data platforms, optimize performance, and lead AI-assisted engineering adoption., * Formal training or certification in software engineering and 5+ years of applied experience.

 * 10+ years of software engineering experience with depth in data engineering and big data platforms.
 * Strong hands-on development experience in Java and proficiency in Python.
 * Proven experience designing and building robust ETL/ELT pipelines and data integration frameworks.
 * Strong experience with Apache Spark, distributed processing concepts, and performance tuning.
 * Knowledge of data storage serialization and formats such as Parquet and Avro.
 * Solid understanding of data lake and lakehouse concepts, including data quality, metadata, and governance.
 * Experience with cloud data warehouses such as Snowflake or equivalent.
 * Demonstrated experience leading responsible use of AI-assisted development tools and setting validation standards for correctness, performance, and security.
 * Ability to lead technical discussions, communicate across stakeholders, and drive delivery in cross-functional environments.

Preferred Skills:

 * Experience with orchestration and workflow scheduling tools such as Airflow, Dagster, or Control-M.
 * Exposure to streaming and event-driven processing, including Kafka and Spark Structured Streaming, and incremental processing patterns such as CDC and upserts.
 * Experience with table formats and ACID layers such as Delta Lake, Apache Iceberg, Hudi.
 * Strong production operations mindset covering on-call, SLAs/SLOs, observability, and incident response.
 * Experience in regulated environments including risk, audit, compliance, privacy, and PII handling.
 * Experience with cost optimization and FinOps practices for data platforms.
 * Experience working with Agentic AI and Gen AI solutions and capabilities.

Must Have Summary:

 * Python and Java.
 * ETL pipeline design and development.
 * AWS.
 * AI tool experience in engineering workflows.

## Description

* Execute creative software solutions, including design, development, and technical troubleshooting for complex data challenges.
 * Drive team adoption of enterprise-authorized AI-assisted engineering practices and establish validation standards including secure coding, peer review, and automated testing.
 * Apply SDLC toolchain and automation, including AI-assisted development capabilities, to increase delivery value.
 * Lead architecture and hands-on delivery of large-scale ETL/ELT pipelines across ingestion, transformation, validation, reconciliation, and publishing.
 * Design and operationalize data lake patterns including partitioning, data quality controls, lineage, governance, and reusable datasets.
 * Build and optimize distributed processing workloads using Apache Spark and modern formats such as Parquet and Avro.
 * Drive performance tuning across compute and storage, including Spark tuning for shuffle, joins, caching, and skew handling.
 * Implement engineering best practices including code quality, automated testing, CI/CD, observability, security-by-design, and operational readiness.
 * Build and maintain Java-based services supporting data workflows, including ingestion services, orchestration helpers, APIs, and data access layers.
 * Develop REST APIs and integration components to enable downstream consumption and platform interoperability.
 * Apply modern application practices including clean architecture, SOLID principles, test automation, and secure coding.