About
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. 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). What You'll Do 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. Orchestration & Reliability: Implement scheduling, monitoring, alerting, and data quality checks to ensure reliable, observable pipelines and trustworthy datasets. Azure Platform Ownership: Lead/advance Azure cloud adoption, integrating Azure DevOps, CI/CD, Infrastructure-as-Code, and Databricks to modernize data engineering workflows. Analytics Enablement: Partner with data scientists and BI teams to deliver end-to-end solutionsfrom ingestion and normalization through model outputs and visualization in Power BI and Tableau. 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. Microsoft Fabric & OneLake: Build governed, discoverable data products leveraging Microsoft Fabric, OneLake, and Spark notebooks. Data Governance: Implement role-based access control and compliance with Microsoft Purview; standardize metadata, lineage, and data stewardship processes. Performance & Cost Optimization: Continuously optimize pipelines, clusters, and storage patterns for performance, scalability, and cost efficiency. Stakeholder Partnership: Work cross-functionally with engineering, security, and business stakeholders to prioritize backlogs and deliver measurable outcomes. Requirements Must-Have Qualifications 7+ years in data engineering (open to mid-to-senior; adjust as needed). Strong hands-on expertise with Azure Data Factory, Azure Databricks, Spark, SQL, and Python. Proven experience building production-grade pipelines for both streaming/batch or transactional/bulk loads. Practical knowledge of Azure DevOps (Repos, Pipelines), CI/CD for data artifacts, and environment promotion. Experience implementing data quality, monitoring/alerting, and observability across pipelines. Familiarity with data governance, RBAC, and cataloging/lineagepreferably with Microsoft Purview. Experience enabling analytics in Power BI and/or Tableau (dataset modeling, performance tuning, refresh strategies). Clear communication skills and ability to work with cross-functional technical and non-technical teams. Nice-to-Have Experience with Microsoft Fabric and OneLake in production or POCs. Exposure to MLOps (model registry, feature stores, batch/real-time scoring). Work with serverless patterns and event-driven data ingestion (e.g., Functions, Event Hubs, Kafka). Knowledge of data modeling (Dimensional, Data Vault, Lakehouse), medallion architectures, and Delta Lake. Performance tuning for Spark clusters, partitioning strategies, and cost optimization in Azure. Experience with security & compliance frameworks and enterprise RBAC design. Familiarity with generative AI integrations or vectorized data workflows
Languages
- English
Notice for Users
This job comes from a TieTalent partner platform. Click "Apply Now" to submit your application directly on their site.