VP Software Engineering at GM Financial responsible for technology implementation strategy and leading product teams in Agile environment. Championing technology modernization and driving operational excellence.
About the role
- Senior Data Product Engineer leading lifecycle development of data products for various internal stakeholders. Ensuring data infrastructure is robust, performant, and future-proof with cutting-edge technologies.
Responsibilities
- Lead the full lifecycle development of data products, including requirements gathering, architectural design, implementation, testing, and deployment
- Ensure data products are discoverable, well-documented, and adhere to strict data quality standards
- Apply a pragmatic combination of Domain-Driven Design (DDD) principles to deeply understand core business contexts and translate them into robust, Kimball-inspired dimensional models that power our data products within Snowflake
- Architect and implement highly efficient, event-driven data pipelines using AWS Kinesis for data ingestion and stream processing, leveraging AWS Lambda for serverless transformations and real-time data delivery
- Develop, optimize, and maintain complex SQL-based data transformations using dbt (Data Build Tool) within our Snowflake data platform, ensuring modularity, testability, and version control for all data models
- Collaborate closely with our AI/ML workbench to design and build data products specifically tailored for machine learning initiatives, including feature engineering, data preparation, and ensuring data readiness for model training and inference
- Establish and enforce best practices for data governance, metadata management, data lineage, and data observability to ensure the reliability and integrity of our data products.
Requirements
- 5+ years of hands-on experience in data engineering, data warehousing, or a similar role
- Expert-level proficiency with Snowflake for data warehousing, performance tuning, and query optimization
- Proficiency in SQL, Java, dbt (Data Build Tool) and modern data platforms (Snowflake, AWS, GCP) for data modeling, transformation, testing, and documentation
- Extensive experience with AWS Lambda and AWS Kinesis for serverless and real-time data processing architectures (CI/CD)
- Strong programming skills in Python (preferred) for data manipulation, scripting, and automation
- Proven experience in designing and preparing data for AI/ML applications
- Deep practical understanding and application of both Kimball's dimensional modeling methodologies and Domain-Driven Design (DDD) concepts in complex data environments
- A genuine passion for treating data as a product, understanding user needs, defining data contracts, and delivering measurable value through high-quality data assets
- Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative field.
Benefits
- A bonus and/or long-term incentive units may be provided as part of the compensation package
- Full range of medical, financial, and/or other benefits, dependent on the level and position offered
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