• Orquestar el desarrollo, escalabilidad y operación de la plataforma de datos en GCP. • Garantizar que el flujo de datos sea eficiente, de alta calidad y alineado con los objetivos del negocio. • Liderar equipos de +5 personas bajo metodologías Agile/Scrum. • Convertir necesidades complejas en soluciones técnicas robustas y escalables. • Visión sistémica enfocada en arquitectura Medallion, optimización de performance y eficiencia de costos. • Conectar y gestionar expectativas con stakeholders no técnicos.

• Collaborate & Strategize: Partner closely with business stakeholders to understand their challenges and design end-to-end architecture that solves complex business problems. • Build & Maintain Data Models: Design, develop, and own robust, efficient, and scalable data models in Snowflake and Iceberg using dbt and advanced SQL. • Orchestrate & Automate: Build and manage reliable data pipelines and CI/CD workflows using tools like Airflow, Python, and Terraform to ensure data is fresh, trustworthy, and infrastructure is version-controlled. • Champion Data Quality: Implement rigorous testing, documentation, and data governance practices to maintain a single source of truth. • Enable Analytics & Workflows: Act as the Product Owner and Tech Lead for your data domains, taking responsibility for the end-to-end data product delivery– from raw ingestion to data models enabling analytics and data apps in tools like Tableau and Retool. • Innovate with AI: Help us build our next-generation data infrastructure by integrating AI capabilities (like Snowflake Cortex AI) to democratize analytics and empower the business. • Architect Observability: Implement monitoring and alerting frameworks (e.g., dbt packages or Monte Carlo monitors) to proactively catch "silent" data failures before stakeholders do.

• Design and scale our data platform including pipelines, models, and orchestration frameworks • Develop scalable ETL/ELT pipelines for ingesting data from APIs, databases, and event streams • Define and implement systems for data ingestion, storage, processing, and transformation • Build and manage workflow orchestration using tools like Airflow • Build semantic layer as well as dashboards • Establish best practices for data modeling, testing, and quality • Partner with stakeholders to shape data requirements and enable BI and analytics use cases • Optimize systems for performance, scalability, and cost from day one and apply software engineering principles (testing, CI/CD, modular design) to data infrastructure

Senior/Lead Azure Data Engineer (Contract — 3 Months) Location: Remote (U.S.), core overlap with CST Engagement: Contract only (C2C) — no C2H for this role Start: Immediate | Duration: 3 months (extension possible) Comp: Competitive hourly rate (DOE) About Smartbridge Smartbridge simplifies business transformation across App Development, Automation, Data & Analytics, and Modernization. We build production systems for clients in Energy, Life Sciences, and Food & Beverage. The Opportunity Our project needs a senior/lead who can both design and build modern Azure data platforms—someone with strong coding in T-SQL and Python/PySpark, architectural judgment, and deep database chops (modeling, performance, reliability). Because this is contract-only, fit must be tight and immediate impact. What You’ll Own (Architecture + Build) - Architecture: Define the target-state Azure data architecture (ingestion, orchestration, storage zones, serving patterns), security/networking boundaries, cost/perf tradeoffs, and promotion strategy (Dev→Test→Prod). - Pipelines & Code: Implement robust ELT/ETL with ADF/Synapse Pipelines (parameters, reusable templates, CI/CD). Hands-on in T-SQL and Python/PySpark for transformations, utilities, and tests. - Database Excellence: Physical/semantic modeling, partitioning, columnstore strategies, statistics management, query plan analysis, index design, concurrency & transaction isolation, workload management. - Observability & Reliability: SLA/SLO definitions, Azure Monitor / Log Analytics / App Insights dashboards and alerts; error handling, retries/backoff, idempotency, CDC and schema drift strategies. - Security & Governance: RBAC, Key Vault, managed identities, private endpoints/VNet, data masking patterns; document data contracts and access patterns. - Leadership: Code reviews, PR discipline, mentoring, and crisp documentation/runbooks for client handoff. Must-Have (Senior-Level) Experience - 8–12+ years in data engineering (recent Azure focus). - Expert with ADF (linked services, datasets, IRs—including self-hosted), Synapse (SQL pools/serverless, pipelines), and ADLS/Blob. - T-SQL: advanced query tuning, execution plan analysis, windowing, TVFs/stored procs, temp tables vs CTE tradeoffs, cardinality estimator know-how. - Python/PySpark: production data transforms, packaging, and testing. - CI/CD: Azure DevOps or GitHub Actions (multi-stage releases, approvals, infra + data deployments). - Proven delivery of production-grade platforms at scale (TB-level data, strict SLAs). Not a fit: Primarily BI/reporting backgrounds without strong pipeline/build + DB performance experience. Nice to Have - Designed and implemented robust data validation procedures to verify the completeness of data transfers, ensuring all records were successfully migrated and proactively triggering alerts in cases of discrepancies. - Experience with working with large SQL tables (100 million rows) - IaC (Bicep/Terraform) for data resources. - Event-driven integration (Service Bus/Event Grid, CDC tooling). - Certs: DP-203 or AZ-204 are a plus.