Senior Data Engineer

6 days ago

Jaipur, India ThoughtsWin Full time

Role Overview : Design, build, and optimize large-scale, production-grade data pipelines and analytics platforms on Azure, leveraging Databricks, Synapse, and the broader Microsoft data ecosystem.Deliver business-critical data assets for analytics, BI, and AI/ML initiatives.Key Technical Responsibilities : - Architect modern data lakes using Azure Data Lake Storage Gen2 for batch and streaming workloads- Build and maintain scalable ETL/ELT pipelines using Azure Data Factory and Databricks (PySpark, Scala, SQL)- Orchestrate data workflows across ADF, Databricks, and Synapse Pipelines; implement modular and reusable data pipeline components- Develop advanced notebooks and production jobs in Azure Databricks (PySpark, SparkSQL, Delta Lake)- Optimize Spark jobs by tuning partitioning, caching, cluster configuration, and autoscaling for performance and cost- Implement Delta Lake for ACID-compliant data lakes and enable time travel/audit features- Engineer real-time data ingestion from Event Hubs, IoT Hub, and Kafka into Databricks and Synapse- Transform and enrich raw data, building robust data models and marts for analytics and AI use cases- Integrate structured, semi-structured, and unstructured sources, including APIs, logs, and files- Implement data validation, schema enforcement, and quality checks using Databricks, PySpark, and tools like Great Expectations- Manage access controls : Azure AD, Databricks workspace permissions, RBAC, Key Vault integration- Enable end-to-end lineage and cataloging via Microsoft Purview (or Unity Catalog if multi-cloud)- Automate deployment of Databricks assets (notebooks, jobs, clusters) using Databricks CLI/REST API, ARM/Bicep, or Terraform- Build and manage CI/CD pipelines in Azure DevOps for data pipelines and infrastructure as code- Containerize and deploy custom code with Azure Kubernetes Service (AKS) or Databricks Jobs as needed- Instrument monitoring and alerting with Azure Monitor, Log Analytics, and Databricks native tools- Diagnose and resolve performance bottlenecks in distributed Spark jobs and pipeline orchestrations- Collaborate with data scientists, BI engineers, and business stakeholders to deliver data solutions- Document design decisions, create technical specifications, and enforce engineering standards across the teamRequired Skills & Experience : - Hands-on with : 1. Azure Data Lake Gen2, Azure Data Factory, Azure Synapse Analytics, Azure Databricks2. PySpark, SparkSQL, advanced SQL, Delta Lake3. Data modeling (star/snowflake), partitioning, and data warehouse concepts- Strong Python programming and experience with workflow/orchestration (ADF, Airflow, or Synapse Pipelines)- Infrastructure automation : ARM/Bicep, Terraform, Databricks CLI/API, Azure DevOps- Deep understanding of Spark internals, cluster optimization, cost management, and distributed computing- Data security, RBAC, encryption, and compliance (SOC2, ISO, GDPR/DPDPA)- Excellent troubleshooting, performance tuning, and documentation skills (ref:hirist.tech)

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