Lead Data Engineer - Columbus, OH

We're Looking for a Lead Data Engineer - Columbus, OH Position type: Contract to Hire Duration: 6+ Months Lead Data engineer - comparable to 603 experience level Must have: python java ETL pipeline AWS AI Tool experience Job responsibilities Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team. Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation Lead architecture and hands-on delivery of large-scale data pipelines (ETL/ELT) covering ingestion, transformation, validation, reconciliation, and publishing across curated layers Design and operationalize data lake patterns, including partitioning strategies, data quality controls, lineage, governance, and reusable datasets/data products Build and optimize distributed processing workloads using Apache Spark and modern storage/file formats (e.g., Parquet, Avro) Drive performance tuning across compute and storage (e.g., Spark tuning: shuffle, joins, caching, skew handling; and warehouse tuning where applicable) Implement engineering best practices: code quality, automated testing, CI/CD, observability (metrics/logs/traces), security-by-design, and operational readiness Build and maintain Java-based services and components that support data workflows (e.g., ingestion services, orchestration helpers, APIs, data access layers) Develop REST APIs and integration components to enable downstream consumption and platform interoperability Apply modern application engineering practices (clean architecture, SOLID principles, test automation, and secure coding practices) Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security. Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices 10+ years of software engineering experience, with strong depth in data engineering/big data platforms Strong hands-on development experience in Java plus proficiency in at least one data-focused language such as Python Proven experience designing and building robust ETL/ELT pipelines and data integration frameworks Strong experience with Apache Spark and distributed processing concepts (fault tolerance, partition