• 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

• Provide technical leadership for large-scale data modernization and migration initiatives. • Define data architectures using Databricks Lakehouse and Microsoft Azure. • Assess legacy environments and determine the best strategy for migrating, refactoring or decommissioning workloads. • Design modern data pipelines, ETL/ELT processes and orchestration strategies. • Establish architecture, quality and governance standards and engineering best practices. • Ensure data quality and consistency throughout the migration process. • Collaborate with architects, data engineers, Databricks specialists and client teams to ensure high-quality deliveries. • Serve as the project's technical reference, supporting architectural decisions and mentoring other engineers. • Contribute to automation initiatives and the use of Artificial Intelligence to accelerate migration processes.

• Traduzir demandas de negócio em problemas e soluções de engenharia de dados. • Projetar, desenvolver e implementar pipelines de dados escaláveis e resilientes. • Construir e manter processos de ingestão, transformação e disponibilização de dados. • Garantir a qualidade, performance e governança das soluções implementadas. • Participar da definição de arquiteturas e padrões de engenharia de dados. • Atuar em conjunto com times multidisciplinares, apoiando iniciativas orientadas a dados. • Realizar troubleshooting e otimização de processos e ambientes de dados.