Data Product Manager (DPM) / Data Engineer - Contractor

KAYGEN•Cupertino, CA

14h•Hybrid

About The Position

We are looking for a Data Product Manager who can operate well beyond writing instrumentation specifications. This is a broad, strategic role that bridges business, analytics, and engineering. You will drive the strategy, design, and delivery of next-generation analytical data products, partnering with stakeholders across multiple lines of business to define the right metrics, semantic layers, and visualizations that leadership relies on to make decisions.

Requirements

8+ years of experience designing, developing, and deploying next-generation analytical data products
3+ years as a Senior IC leading the development of data products and visualizations across multiple lines of business
Proficiency in SQL and Scala with hands-on experience in data pipeline tools (Apache Spark, Kafka, Airflow), CI/CD practices, and version control
Strong understanding of data warehousing, data modeling (dimensional/star schemas), and metric standardization
Strong hands-on experience in SQL, Spark, and Scala and data pipeline tools
Excellent executive communication skills with both technical and non-technical audiences

Nice To Haves

Strong hands-on experience with SQL, Spark, and Scala in a production environment
Experience building and maintaining large-scale data pipelines with a focus on scalability, performance, reliability, and data quality
Experience with modern data platforms and lakehouse technologies (Iceberg, Trino, Superset)
Good understanding of data modeling, analytics, and metric design
Good problem-solving skills with ability to investigate complex data issues and derive actionable insights
Exposure to AI/ML, recommendation systems, feature engineering, GenAI, RAG, or other AI-powered data products (role is not exclusively AI-focused)

Responsibilities

Own the strategy, design, and end-to-end delivery of analytical data products and visualizations
Bridge business, analytics, and engineering teams, translating business needs into well-defined instrumentation, metrics, and data models
Define and standardize metrics and semantic layers across multiple lines of business
Engage executive and cross-functional stakeholders, communicating effectively with both technical and non-technical audiences
Partner with engineering on data pipeline design, modeling, and delivery workflows