Position Summary: The Excel Analytics team is growing, and we are seeking a Data Engineer to help build and maintain the data pipelines and platforms that power our reporting, modeling, and client deliverables. This is a hands-on role for an early-career engineer who takes pride in building things the right way—with testing, data quality, and reliability built in from the start, not bolted on later. You will work alongside senior engineers and analysts to ingest, transform, and deliver trustworthy data across the business. This role will be based out of the Excel Chicago office.

Role & Responsibilities:

• Build, maintain, and optimize data pipelines that reliably ingest, transform, and export data from internal and external sources.
• Write and maintain automated tests—unit, integration, and data-quality checks—to ensure pipelines and datasets are correct, complete, and trustworthy.
• Develop Python-based data workflows and orchestration tasks following established team patterns.
• Contribute to relational data design—tables and relationships, primary/foreign keys, and appropriate indexing—and deliver schema changes as version-controlled migrations.
• Support the data lake and warehouse layer (S3, Athena/Glue, PostgreSQL/Aurora), helping keep schemas, models, and documentation accurate.
• Contribute to CI/CD pipelines and containerized (Docker) workflows, ensuring changes are tested and deployed safely.
• Investigate and resolve data and pipeline issues, and help improve monitoring so problems are caught early.
• Provide production support for data pipelines during standard working hours.
• Collaborate with analysts, engineers, and client-facing teams to turn business needs into clean, documented solutions.

Education and Experience:

• A four-year degree in Computer Science, Data Science, Mathematics, Engineering, or a related field OR equivalent experience.
• 2+ years of professional experience in data engineering, software engineering, or a closely related role.

Required Qualifications:

• Proficiency in Python and SQL, with hands-on experience building or maintaining data pipelines.
• Demonstrated commitment to testing—writing unit and integration tests and validating data quality (e.g., pytest, schema/row-level checks, or similar).
• Experience with OLTP (row-oriented) databases (PostgreSQL, MySQL, or equivalent).
• Experience with OLAP (columnar) databases (Clickhouse, Redshift or equivalent).
• Solid grasp of data modeling, indexing, and schema migrations.
• Working knowledge of cloud environments (AWS preferred; GCP/Azure acceptable).
• Familiarity with Git and CI/CD pipelines, and an understanding of data and software engineering best practices.
• Exposure to AI/ML or Generative AI/LLM-driven solutions.
• Awareness of data security, access controls, and observability/monitoring practices.
• Ability to work collaboratively, take ownership of your work, and operate in a fast-paced environment.

Knowledge, Skills and Abilities:

• Experience with Apache Airflow or other workflow orchestration tools.
• Familiarity with Terraform or Infrastructure-as-Code.
• Familiarity with event-driven or serverless architectures (e.g., S3/SQS-triggered pipelines, Lambda).
• Experience building APIs or services to expose data (FastAPI, Flask, or similar).
• Experience with BI tools (Power BI, Tableau).
• Experience working in the sports industry and/or the agency world.
• Strong interest in sports and sports analytics.
• Familiarity with marketing data (e.g., campaign, audience, engagement, and brand/sponsorship metrics);

The pay range for this position is: $100,000 per year - $115,000 per year. This position is also eligible for benefits and discretionary bonus.

Ultimately, the salary may vary based upon, but not limited to, relevant experience, time in role, business sector, and geographic location, among other criteria.