Note: The job is a remote job and is open to candidates in USA. BetMGM is revolutionizing sports betting and online gaming in the United States and Canada. 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.
Responsibilities
- 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 ClickOps in production
- 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 (GitLab CI or GitHub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI-assisted code review
- Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ACCOUNT_USAGE; 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
- 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
- Direct AI coding agents (Claude Code, Cursor, GitHub 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
- 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
Skills
- BS or MS in Computer Science, Statistics, Math, or other STEM field — or equivalent practical experience. Practical experience wins ties
- 5+ years building production data pipelines on a modern stack (Python + SQL + dbt + cloud)
- Deep Snowflake — beyond SQL into administration: warehouse sizing, RBAC, resource monitors, Streams/Tasks, Dynamic Tables, secure data sharing, cost tuning via ACCOUNT_USAGE
- Strong AWS — S3, ECS/Fargate, Lambda, IAM, Secrets Manager, VPC — plus production experience with at least one of EMR Serverless, Glue, or MWAA
- Terraform for both cloud and Snowflake — you have owned IaC, not just touched it
- Orchestration fluency — Prefect, Airflow, or Dagster — and an opinion about when each is the right tool
- CI/CD ownership — you have built quality gates that block bad code, not just YAML pipelines that pass
- Bias toward outcomes — you describe past work in terms of SLAs, incidents, and customers served, not tool checklists
- Snowflake-native ML (Snowpark, Cortex AISQL, Snowflake Notebooks) for in-warehouse scoring or unstructured workloads
- Iceberg / open-table-format experience for cross-engine interoperability
- Streaming experience — Kafka, Snowpipe Streaming, or Kinesis — with stated latency budgets
- Reverse-ETL exposure (Hightouch, Census, or custom) into operational marketing or product systems
- A demonstrable track record of shipping more with AI in the loop than without — not 'I have used Cursor,' but 'this is how I design work for an agent to do.'
- Regulated-industry experience (gaming, fintech, healthcare) — comfort with audit, lineage, and PII tiering
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!
- This position is also eligible for participation in a performance-based bonus plan.
Company Overview