Design, build, and maintain scalable cloud-native data pipelines and dimensional data models using DBT, SQL, and AWS services. Implement infrastructure-as-code with AWS CDK, automate CI/CD, ensure data quality and governance, optimize performance, and collaborate with cross-functional teams and offshore partners to deliver analytics-ready data assets.
Job Title: Data Engineer
Location: Remote
Experience- 4-6 years
Overview /Objective:
We are seeking a skilled 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.
ResponsibilitiesResponsibilities:
- 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
- Perform comprehensive data profiling on staging datasets to assess completeness, accuracy, consistency, timeliness, and conformity with business rules before downstream ingestion.
- Conduct gap analysis between source, staging, and target data models to identify missing attributes, mismatched definitions, and transformation issues impacting reporting and analytics.
- Partner with business SMEs, product owners, and analytics teams to clarify data definitions, resolve ambiguities, and prioritize remediation of critical data gaps.
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
- 4+ 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.
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