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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata warehousingSQLJavadbtAWS LambdaAWS KinesisPythondata modelingdata transformation

Soft Skills

communicationcollaborationproblem-solvinguser needs understandingdata governancemetadata managementdata lineagedata observabilitypassion for data as a productarticulating technical concepts

Certifications

Bachelor's degree in Computer ScienceMaster's degree in Computer ScienceBachelor's degree in EngineeringMaster's degree in EngineeringBachelor's degree in Data ScienceMaster's degree in Data Science