Senior Data Engineer
<p><br></p><p>We’re looking for a hands-on Data Engineer to build and scale cloud-native data platforms on Azure. You’ll design and maintain reliable ETL/ELT pipelines, enable governed cross-departmental data access, and collaborate closely with analytics and data science teams to power forecasting, predictive analytics, and executive reporting.</p><p>This role is ideal for someone who thrives at the intersection of modern data engineering (ADF, Spark, Databricks, SQL, Python), platform governance (Purview, RBAC), and analytics enablement (Power BI/Tableau, Microsoft Fabric).</p><p>What You’ll Do</p><ul><li>Build Scalable Pipelines: Design, implement, and maintain ETL/ELT pipelines using Azure Data Factory, Spark, Databricks, SQL, and Python for both transactional and bulk data loads.</li><li>Orchestration & Reliability: Implement scheduling, monitoring, alerting, and data quality checks to ensure reliable, observable pipelines and trustworthy datasets.</li><li>Azure Platform Ownership: Lead/advance Azure cloud adoption, integrating Azure DevOps, CI/CD, Infrastructure-as-Code, and Databricks to modernize data engineering workflows.</li><li>Analytics Enablement: Partner with data scientists and BI teams to deliver end-to-end solutions—from ingestion and normalization through model outputs and visualization in Power BI and Tableau.</li><li>AI/ML Collaboration: Productionize features and datasets for forecasting & predictive analytics, supporting models such as RNNs and generative AI workloads; contribute to MLOps best practices.</li><li>Microsoft Fabric & OneLake: Build governed, discoverable data products leveraging Microsoft Fabric, OneLake, and Spark notebooks.</li><li>Data Governance: Implement role-based access control and compliance with Microsoft Purview; standardize metadata, lineage, and data stewardship processes.</li><li>Performance & Cost Optimization: Continuously optimize pipelines, clusters, and storage patterns for performance, scalability, and cost efficiency.</li><li>Stakeholder Partnership: Work cross-functionally with engineering, security, and business stakeholders to prioritize backlogs and deliver measurable outcomes.</li></ul><p><br></p>
<p>Must-Have Qualifications</p><ul><li>7+ years in data engineering (open to mid-to-senior; adjust as needed).</li><li>Strong hands-on expertise with Azure Data Factory, Azure Databricks, Spark, SQL, and Python.</li><li>Proven experience building production-grade pipelines for both streaming/batch or transactional/bulk loads.</li><li>Practical knowledge of Azure DevOps (Repos, Pipelines), CI/CD for data artifacts, and environment promotion.</li><li>Experience implementing data quality, monitoring/alerting, and observability across pipelines.</li><li>Familiarity with data governance, RBAC, and cataloging/lineage—preferably with Microsoft Purview.</li><li>Experience enabling analytics in Power BI and/or Tableau (dataset modeling, performance tuning, refresh strategies).</li><li>Clear communication skills and ability to work with cross-functional technical and non-technical teams.</li></ul><p>Nice-to-Have</p><ul><li>Experience with Microsoft Fabric and OneLake in production or POCs.</li><li>Exposure to MLOps (model registry, feature stores, batch/real-time scoring).</li><li>Work with serverless patterns and event-driven data ingestion (e.g., Functions, Event Hubs, Kafka).</li><li>Knowledge of data modeling (Dimensional, Data Vault, Lakehouse), medallion architectures, and Delta Lake.</li><li>Performance tuning for Spark clusters, partitioning strategies, and cost optimization in Azure.</li><li>Experience with security & compliance frameworks and enterprise RBAC design.</li><li>Familiarity with generative AI integrations or vectorized data workflows</li></ul><p><br></p>
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- Houston, TX
- remote
- Temporary
-
- USD / Hourly
- <p><br></p><p>We’re looking for a hands-on Data Engineer to build and scale cloud-native data platforms on Azure. You’ll design and maintain reliable ETL/ELT pipelines, enable governed cross-departmental data access, and collaborate closely with analytics and data science teams to power forecasting, predictive analytics, and executive reporting.</p><p>This role is ideal for someone who thrives at the intersection of modern data engineering (ADF, Spark, Databricks, SQL, Python), platform governance (Purview, RBAC), and analytics enablement (Power BI/Tableau, Microsoft Fabric).</p><p>What You’ll Do</p><ul><li>Build Scalable Pipelines: Design, implement, and maintain ETL/ELT pipelines using Azure Data Factory, Spark, Databricks, SQL, and Python for both transactional and bulk data loads.</li><li>Orchestration & Reliability: Implement scheduling, monitoring, alerting, and data quality checks to ensure reliable, observable pipelines and trustworthy datasets.</li><li>Azure Platform Ownership: Lead/advance Azure cloud adoption, integrating Azure DevOps, CI/CD, Infrastructure-as-Code, and Databricks to modernize data engineering workflows.</li><li>Analytics Enablement: Partner with data scientists and BI teams to deliver end-to-end solutions—from ingestion and normalization through model outputs and visualization in Power BI and Tableau.</li><li>AI/ML Collaboration: Productionize features and datasets for forecasting & predictive analytics, supporting models such as RNNs and generative AI workloads; contribute to MLOps best practices.</li><li>Microsoft Fabric & OneLake: Build governed, discoverable data products leveraging Microsoft Fabric, OneLake, and Spark notebooks.</li><li>Data Governance: Implement role-based access control and compliance with Microsoft Purview; standardize metadata, lineage, and data stewardship processes.</li><li>Performance & Cost Optimization: Continuously optimize pipelines, clusters, and storage patterns for performance, scalability, and cost efficiency.</li><li>Stakeholder Partnership: Work cross-functionally with engineering, security, and business stakeholders to prioritize backlogs and deliver measurable outcomes.</li></ul><p><br></p>
- 2026-01-25T17:18:39Z