Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.
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.
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
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.
Similar Jobs
Professional Services • Software
Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.
Top Skills:
AirflowAzureCypherDagsterDbtDockerGremlinHelmJavaKubernetesPythonSalesforceServicenowSparqlSQL
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Design, build, and maintain enterprise ETL and data transformation pipelines to support Medicaid analytics and federal reporting. Optimize data processing with Python, Spark/Databricks, and relational platforms; ensure data validation, reconciliation, auditability, and production support. Collaborate across architects, analysts, QA, and BI teams during cloud migration and modernization efforts.
Top Skills:
Azure Data FactoryAzure DevopsBashCi/CdDatabricksGitInformatica PowercenterOraclePowershellPythonRest ApiSnowflakeSparkSQLSQL ServerTeradata
eCommerce • Healthtech • Kids + Family • Retail • Social Media
Design and scale data pipelines and ML/LLM systems, build agentic automation for pipeline generation and maintenance, improve data monitoring, and collaborate with analysts, product, and ML teams to deliver reliable end-to-end data and AI infrastructure for a high-growth e-commerce platform.
Top Skills:
AirflowAws Ec2Aws EksAws LambdaAws S3DbtLlmsMcp ServersMl PipelinesPythonRagSnowflake
What you need to know about the Los Angeles Tech Scene
Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
Key Facts About Los Angeles Tech
- Number of Tech Workers: 375,800; 5.5% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Snap, Netflix, SpaceX, Disney, Google
- Key Industries: Artificial intelligence, adtech, media, software, game development
- Funding Landscape: $11.6 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Strong Ventures, Fifth Wall, Upfront Ventures, Mucker Capital, Kittyhawk Ventures
- Research Centers and Universities: California Institute of Technology, UCLA, University of Southern California, UC Irvine, Pepperdine, California Institute for Immunology and Immunotherapy, Center for Quantum Science and Engineering
