> Markdown version of [/jobs/ext/3762340-lead-data-engineer](https://www.wearedevelopers.com/jobs/ext/3762340-lead-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - **Company:** Aptino, Inc. - **Location:** Cincinnati, OH, United States - **Experience:** Expert - **Contract:** Contract - **Skills:** Automation of Tests, Microsoft Azure, Big Data, Code Review, Data Architecture, Data Validation, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Software Design Patterns, Distributed Computing Environment, Python (Programming Language), Performance Tuning, Cloud Services, Software Deployment, SQL Stored Procedures, SQL Databases, Enterprise Data Management, Data Processing, Google Cloud, Cloud Platform System, Netezza, Apache Spark, Caching, Data Lakes, Pyspark, Ibm Netezza, Software Coding, Data Pipelines, Databricks - **Published:** October 15, 2026 - **Apply:** https://www.dice.com/job-detail/8710f2aa-b997-43ab-bd89-65d5efa65ca0 ## About the Role We are seeking an experienced Lead Data Engineer with strong hands-on expertise in Databricks, Apache Spark, SQL, and Python/PySpark to lead enterprise data modernization initiatives, particularly migrating legacy data warehouse platforms such as IBM Netezza to Databricks. The ideal candidate will combine technical leadership, large-scale data engineering expertise, and experience managing distributed onshore and offshore teams., * 8+ years of experience in data engineering, data warehousing, ETL/ELT development, or analytics engineering. * 2+ years of experience leading onshore/offshore teams and coordinating distributed engineering groups. * Strong hands-on expertise in Databricks (mandatory), Apache Spark, SQL, Python, and PySpark. * Experience with IBM Netezza or comparable enterprise data warehouse platforms. * Proven experience in data warehouse migration, cloud data platform modernization, or legacy system transformation. * Strong understanding of data pipelines, Lakehouse architecture, distributed processing, and data modeling. * Familiarity with Azure and Google Cloud Platform (Google Cloud Platform). * Excellent communication, technical leadership, problem-solving, and troubleshooting skills. Preferred Skills: * Experience migrating Netezza SQL workloads and legacy ETL processes to Databricks. * Knowledge of Spark optimization, partitioning, caching, job performance tuning, and scalable data processing. * Familiarity with Databricks workflows, Delta Lake, and modern Lakehouse design patterns. * Experience with migration testing, data reconciliation, production deployment, and cutover planning. * Ability to manage multiple vendors, mentor engineers, and deliver projects in a collaborative, onsite environment. Important: Hands-on Databricks expertise is a mandatory requirement for this role. ## Description * Lead onshore and offshore data engineering teams across client organizations, implementation partners, and multiple vendors. * Drive end-to-end migration of legacy data warehouse workloads from IBM Netezza to Databricks. * Convert business requirements and architecture designs into technical specifications, development plans, and engineering deliverables. * Design, develop, and optimize scalable ETL/ELT pipelines, data transformations, and workflows using Databricks, Spark, SQL, and Python/PySpark. * Modernize legacy SQL queries, stored procedures, scripts, and data processing logic for execution on Databricks. * Implement data quality checks, automated testing, performance tuning, and coding standards to ensure reliable and efficient data pipelines. * Coordinate with architects, project managers, business stakeholders, and vendor teams to manage technical dependencies and delivery milestones. * Mentor junior and mid-level engineers, conduct code reviews, and provide technical guidance. * Support deployment activities, production troubleshooting, incident resolution, and ongoing pipeline maintenance. * Lead data validation, source-to-target reconciliation, migration cutover, and post-deployment stabilization. * Leverage Azure and Google Cloud Platform cloud services to support data integration, storage, and modernization initiatives.