Lead data engineering teams to build and support scalable, cloud-first data platforms using lakehouse and modern pipeline technologies.

Direct Agile teams delivering scalable data engineering solutions across full-stack and cloud platforms
Guide a team of developers, data analysts, and data scientists with experience in machine learning, distributed microservices, lakehouse architecture, and full-stack systems
Stay hands-on by reviewing architecture designs, data models, and pipeline code across Python, Spark, Databricks, and Snowflake environments
Drive technical patterns and standards for code quality, maintainability, and reusability across data platforms and pipelines
Oversee the design and health of data platforms and pipelines, setting expectations for scalability, resilience, data quality, and operational efficiency
Coordinate with product managers and software engineers to deliver robust cloud-first data solutions
Communicate technical concepts and data outcomes clearly to internal and external stakeholders to maintain alignment
Serve as a force-multiplier through leading, mentoring, and growing a diverse team of data engineers
Lead end-to-end, large-scale transformative data initiatives, making critical architectural decisions and evaluating platform choices (for example, Snowflake vs Databricks) based on technical and business needs
Contribute to internal and external technology communities through experimentation, learning, and mentoring within the data community

Bachelor’s degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
6+ years of experience in application development (internship experience does not apply)
3+ years of people management experience
2+ years driving technical delivery of roadmap features
4+ years programming with at least one of: Python, Java, or Scala
4+ years designing and developing data pipelines
2+ years in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Master’s Degree in Computer Science or a related field
9+ years of experience in application development with demonstrated proficiency in Python, SQL, Scala, or Java
5+ years hands-on experience designing, deploying, and operating data workloads in at least one public cloud (AWS, Microsoft Azure, or Google Cloud)
5+ years building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks
5+ years designing, implementing, and operating real-time or streaming data pipelines
3+ years data observability experience (e.g., Monte Carlo, Splunk) or data orchestration (e.g., Airflow, Dagster)
5+ years working with unstructured or semistructured data using NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB)
5+ years designing and supporting data warehousing solutions (e.g., Snowflake, Redshift)
3+ years working in an Agile development environment
3+ years developing user-centric reusable data products

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being

Experience: 6+ years • Education: Bachelor’s degree in Computer Science or related quantitative field

Senior Manager Data Engineer

Job Description