Senior Data Engineer

    Highlights

    3+ years' extensive experience with DBT or similar data transformation tools, including building complex & maintainable DBT models and developing DBT packages/macros. You will architect and build scalable data infrastructure that transforms raw data into high-value assets, powering analytics across digital products, fan engagement, and marketing domains.

    Numbers & Facts

    LocationCA

    Description

    Overview /Objective:

    We are seeking a seasoned Data Engineer to join our Sports Analytics & Engineering Practice. This role is pivotal in shaping and implementing our client's vision for a cutting-edge, cloud-native data ecosystem. You will architect and build scalable data infrastructure that transforms raw data into high-value assets, powering analytics across digital products, fan engagement, and marketing domains. Your work will directly contribute to the development of a world-class customer data platform.

    Required Qualifications:

    • Bachelor's or Master's (preferred) degree in a quantitative or technical field such as Statistics, Mathematics, Computer Science, Information Technology, Computer Engineering or equivalent
    • 5+ years of experience in data engineering and analytics on modern data platforms
    • 3+ years' extensive experience with DBT or similar data transformation tools, including building complex & maintainable DBT models and developing DBT packages/macros
    • Deep familiarity with dimensional modeling/data warehousing concepts and expertise in designing, implementing, operating, and extending enterprise dimensional models
    • Understand change data capture concepts
    • Experience working with AWS Services (Lambda, Step Functions, MWAA, Glue, Redshift)
    • Hands-on experience with AWS CDK, CodeCommit, and CodePipeline for infrastructure automation and CI/CD
    • Python proficiency or general knowledge of Jinja templating in Python and/or PySpark
    • Agile experience and willingness to work with extended offshore teams and assist with design and code reviews with customer
    • A great teammate and self-starter, strong detail orientation is critical in this role.

    Responsibilities:

    • Design and build robust, scalable data transformation pipelines using SQL, DBT, and Jinja templating
    • Develop and maintain data architecture and standards for Data Integration and Data Warehousing projects using DBT and Amazon Redshift
    • Collaborate with cross-functional teams to gather requirements and deliver dimensional data models that serve as a single source of truth
    • Own the full stack of data modeling in DBT to empower analysts, data scientists, and BI engineers
    • Enhance and maintain the analytics codebase, including DBT models, SQL scripts, and ERD documentation
    • Ensure data quality, governance alignment, and operational readiness of data pipelines
    • Apply software engineering best practices such as version control, CI/CD, and code reviews
    • Optimize SQL queries for performance, scalability, and maintainability across large datasets
    • Implement best practices for SQL performance tuning, including partitioning, clustering, and materialized views
    • Build and manage infrastructure as code using AWS CDK for scalable and repeatable deployments. Integrate and automate deployment workflows using AWS CodeCommit, CodePipeline, and related DevOps tools
    • Support Agile development processes and collaborate with offshore teams

    Responsibilities:

    • Design and build robust, scalable data transformation pipelines using SQL, DBT, and Jinja templating
    • Develop and maintain data architecture and standards for Data Integration and Data Warehousing projects using DBT and Amazon Redshift
    • Collaborate with cross-functional teams to gather requirements and deliver dimensional data models that serve as a single source of truth
    • Own the full stack of data modeling in DBT to empower analysts, data scientists, and BI engineers
    • Enhance and maintain the analytics codebase, including DBT models, SQL scripts, and ERD documentation
    • Ensure data quality, governance alignment, and operational readiness of data pipelines
    • Apply software engineering best practices such as version control, CI/CD, and code reviews
    • Optimize SQL queries for performance, scalability, and maintainability across large datasets
    • Implement best practices for SQL performance tuning, including partitioning, clustering, and materialized views
    • Build and manage infrastructure as code using AWS CDK for scalable and repeatable deployments. Integrate and automate deployment workflows using AWS CodeCommit, CodePipeline, and related DevOps tools
    • Support Agile development processes and collaborate with offshore teams

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