• Design, build, and maintain robust, scalable ELT/ETL data pipelines (batch and streaming) from various source systems into cloud data platforms and warehouses. • Optimize pipelines for performance, cost, reliability, and scalability. • Design and implement conceptual, logical, and physical data models (including dimensional modeling, star/snowflake schemas). • Build and maintain transformation layers using modern tools (e.g., dbt) to create clean, well-documented, analytics-ready datasets. • Write optimal SQL queries for data exploration, ad-hoc analysis, and troubleshooting. • Support the creation of reports, dashboards, and self-service analytics assets in collaboration with data analysts and business teams. • Translate business questions into data requirements and deliver actionable insights or datasets. • Monitor data pipelines and data delivery processes to ensure SLAs for timeliness, freshness, and accuracy are consistently met. • Proactively identify, troubleshoot, and resolve data issues impacting downstream consumers or business operations. • Manage incidents related to data availability and quality; participate in on-call rotations as needed. • Document data pipelines, models, lineage, and processes.

• Support SQL Server databases, including query development, troubleshooting, and maintenance. • Maintain and support ETL processes and data integrations. • Provide operational support for Azure services and cloud infrastructure. • Monitor system performance and resolve production issues. • Support enterprise applications, reporting platforms, and file transfer processes. • Collaborate with technical teams to ensure data availability and system reliability. • Support Windows and Linux server environments.

• 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; configure Monte Carlo coverage for volume, freshness, schema, and distribution; triage incidents end-to-end. • Collaborate with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

• Define and own the strategic architecture of Foodsmart's data platform • Lead the design and implementation of next-generation data pipelines • Establish company-wide standards for data quality, governance, security, and compliance • Act as a cross-functional technical leader, partnering with engineering, product, data science, and business stakeholders • Identify systemic bottlenecks and drive platform-wide improvements • Mentor and level up engineers; influence hiring decisions and data platform strategy • Serve as an internal authority on data engineering best practices