Senior Data Engineer

## Senior Data Engineer Apply remote type:
Partially Remote locations:
Hybrid, New Jersey time type:
Full time posted on:
Posted 3 Days Agojob requisition :
JR100736
** 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.
As a valued team member, we’re committed to giving you the resources and support you need to thrive. Our benefits and perks include:
* 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!

At BetMGM, we recognize that every individual plays a meaningful role in our success. That’s why we’re committed to building a respectful, inclusive workplace. It’s the strategy behind every win. By meeting people where they are, we create a culture of belonging where everyone can thrive and a workplace that reflects our values, our people, and our drive to win.
** 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…