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BetMGM

Senior Data Engineer

BetMGM

Senior Data Engineer at BetMGM transforming raw data into reliable datasets for analytics. Collaborating on data engineering projects in a dynamic online gaming environment.

Posted 6/19/2026full-timeNew Jersey • New Jersey • 🇺🇸 United StatesSenior💰 $135,000 - $170,000 per yearWebsite

Tech Stack

Tools & technologies
AirflowAWSCloudETLKafkaPythonSQLTerraform

About the role

Key responsibilities & impact
  • Own the path from raw transactional and event data to trustworthy, well-modeled datasets powering BetMGM's analytics, ML, and operational systems.
  • 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.
  • Own freshness SLAs and data contracts for the gold layer; triage incidents end-to-end.
  • Direct AI coding agents as a force multiplier — writing specs, decomposing work, reviewing AI-generated PRs, and owning the architectural decisions agents cannot make.
  • Partner with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

Requirements

What you’ll need
  • BS or MS in Computer Science, Statistics, Math, or other STEM field — or equivalent practical experience.
  • 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.
  • Nice-to-Haves: 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.'

Benefits

Comp & perks
  • 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!

ATS Keywords

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Hard Skills & Tools
PythonSQLdbtSnowflakeAWSTerraformCI/CDKafkaEMR ServerlessIceberg
Soft Skills
outcome-orientedincident triagecollaborationarchitectural decision-makingspec writingwork decompositioncode reviewstandards partnership