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Senior Data Engineer

Job in Washington, District of Columbia, 20022, USA
Listing for: BetMGM
Full Time position
Listed on 2026-07-14
Job specializations:
  • Software Development
    Data Engineering, AWS
Salary/Wage Range or Industry Benchmark: 135000 - 170000 USD Yearly USD 135000.00 170000.00 YEAR
Job Description & How to Apply Below

Discover What’s Possible At BetMGM

Ready to make your career legendary? Join us as we bring the magic of Vegas to our players. The BetMGM team has over 1,400 talented members, revolutionizing sports betting and online gaming in the United States and Canada. We’re a brand with technology at our hearts and the most driven and focused talent in the business.

Benefits
  • Medical, Dental, Vision, Life, and Disability Insurance
  • 401(k) with company match
  • Pre‑tax spending accounts including health care FSA and commuter savings
  • Flexible paid time off
  • Professional development reimbursement and ongoing skills training opportunities
  • Employee resource groups
  • Swag, ticket giveaways, and more!
About

The Role

The Senior Data Engineer will own the path from raw transactional and event data to trustworthy, well‑modeled datasets powering BetMGM's analytics, ML, and operational systems. Builds on an AWS + Snowflake stack — Prefect on ECS Fargate for orchestration, dbt for transformation, Terraform for everything, CI/CD pipelines with quality gates that block bad code. Comfortable directing AI coding agents (Claude Code, Cursor, Copilot, dbt Copilot, Snowflake Cortex Code) as a force multiplier across the engineering workflow — PR review, model authoring, test generation, incident triage.

Strong opinions about what belongs in the warehouse vs. the orchestrator vs. the platform, and the seniority to push back when a request shouldn't be built the way it was asked.

Responsibilities Pipeline & Platform Engineering
  • Design, build, and operate batch, micro‑batch, and streaming pipelines feeding Snowflake — Prefect‑orchestrated flows on ECS Fargate, dbt for transformation, Snowpipe Streaming and Kafka for event ingestion.
  • Own the full dbt lifecycle (sources → staging → intermediate → marts) with model contracts, freshness SLAs, automated tests, and version‑controlled documentation.
  • Stand up Snowflake objects (warehouses, RBAC, resource monitors, Dynamic Tables, Iceberg tables) through Terraform — no Click Ops in production.
AWS Platform Ownership
  • Build AWS‑native infrastructure for data workloads — S3, ECS Fargate, Lambda, EMR Serverless, Glue Catalog, IAM, Secrets Manager, VPC endpoints — entirely in Terraform.
  • Maintain CI/CD pipelines (Git Lab CI or Git Hub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI‑assisted code review.
Snowflake Depth
  • Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ; right‑size before scaling up.
  • Design RBAC, masking policies, and row‑access policies that satisfy a regulated operator without becoming an access bottleneck.
  • Bring newer Snowflake capabilities to bear — Dynamic Tables, Snowpipe Streaming, Iceberg, Cortex AISQL — when they are the right answer, not because they are new.
Data Quality & Observability
  • Own freshness SLAs and data contracts for the gold layer; configure Monte Carlo coverage for volume, freshness, schema, and distribution; triage incidents end‑to‑end.
  • Treat the warehouse as a product: every consumer‑facing model has tests, documentation, an owner, and a defined SLO.
AI in the Engineering Loop
  • Direct AI coding agents (Claude Code, Cursor, Git Hub Copilot, dbt Copilot, Snowflake Cortex Code) as a force multiplier — writing specs, decomposing work, reviewing AI‑generated PRs, and owning the architectural decisions agents cannot make.
  • Help the team raise its ceiling on what is possible with AI in the loop, not just its baseline productivity.
Collaboration
  • Partner with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).
  • Work alongside Entain India and contractor engineering partners; level them up on the standard playbook so the same code review, IaC, and CI/CD norms apply everywhere.
  • Translate stakeholder requests into the right shape — push back when a request should not be built the way it was asked.
Qualifications
  • BS or MS in Computer Science, Statistics, Math, or other STEM field — or equivalent practical experience. Practical experience wins ties.
Must‑Haves
  • 5+ years building production data pipelines on a modern…
Position Requirements
10+ Years work experience
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