AWS Data Engineer

Job Description: 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.

Responsibilities: 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

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.

Qualifications: 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.