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Senior Engineering Manager, Data Engineering
Skills
Computer ScienceAmazon Web ServicesBusinessEvolutionFinanceManaging ManagersOptimization
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- 10+ years building and operating large-scale data platforms analytics systems or software platforms
- 5+ years leading Data Engineering teams with experience managing managers or multiple teams preferred
- Proven ability to build mentor and scale high-performing engineering organizations
- Demonstrated success defining technical strategy driving execution and delivering complex cross-functional initiatives
- Strong experience partnering with Product Analytics Data Science and executive stakeholders
- Deep experience with modern cloud-based analytics platforms including data warehouses lakehouses ELT ETL pipelines and data processing systems
- Strong understanding of data modeling analytics architecture semantic layers and scalable data product design
- Experience implementing data quality observability governance metadata lineage and access control capabilities
- Experience modernizing enterprise data platforms and driving adoption of modern data engineering practices
- Strong understanding of distributed data systems cloud-native architectures and software engineering practices
- Experience with modern data technologies such as Databricks Spark Airflow Kafka Redshift Snowflake Python SQL Graph DB and AWS
- Experience with CI/CD infrastructure automation testing frameworks monitoring and performance optimization
- Bachelor’s degree in Computer Science or a similar discipline preferred
Nice to have
- Healthcare industry experience
- Highly governed industries
- AI and machine learning workflows
- Exceptional leadership
- Strong stakeholder management
- Mentoring future technical leaders
- Comfort with ambiguity
Day to day
- Lead and grow a high-performing Data Engineering organization that designs, builds, and operates Omada's enterprise data platform.
- Drive the strategy and delivery of scalable data pipelines and trusted data products that power analytics reporting experimentation and AI-driven insights.
- Partner across Product Analytics Data Science Governance and Engineering to translate business needs into reliable governed and reusable data capabilities.