& Summary:A career within ..............................

Responsibilities: Design, build, and maintain robust ETL/ELT pipelines on Azure using Apache Spark (PySpark/Scala) on Databricks/ HDInsight • Orchestrate complex data workflows with Azure Data Factory (pipelines, triggers, integration runtimes) and Databricks Jobs/Workflows • Develop and manage data lakes on Azure Blob Storage/ADLS Gen2, including naming, partitioning, lifecycle policies, and schema evolution • Implement curated layers (raw, staged, curated), leveraging columnar formats (Parquet/ORC/Avro) and table formats (Delta Lake); manage metastore/Unity Catalog • Optimize Spark jobs and cluster configurations for performance and cost (autoscaling, spot VMs, Photon, adaptive query execution, caching, partition tuning) • Operationalize jobs with monitoring, logging, and alerting via Azure Monitor, Log Analytics, and Databricks metrics; build runbooks and dashboards • Implement data quality, testing, and observability for pipelines (unit/integration tests, Great Expectations, SLAs, lineage) • Collaborate with Analytics, Data Science, and Product to deliver modeled, trustworthy datasets for BI, ML, and applications • Enforce security and governance best practices (AAD RBAC, Managed Identities, ACLs, Key Vault, Private Endpoints, VNet integration, encryption with CMK/SSE) • Contribute to infrastructure as code and CI/CD (Azure DevOps or GitHub Actions) including Databricks objects deployment • Participate in on-call rotations, incident response, and postmortems; drive continuous improvement and documentation Man

Mandatory skill sets:

6+ years as a Data Engineer (or similar) with a strong focus on Azure data services • Expert-level experience with Apache Spark (PySpark and/or Scala) and distributed data processing • Hands-on experience with Databricks and Azure HDInsight for large-scale batch processing • Proficient in Azure Data Factory for orchestration (pipelines, data flows, triggers) • Strong Python skills and solid SQL (window functions, performance tuning, optimization) • Practical experience with Azure Blob Storage/ADLS Gen2, Azure Key Vault, Azure Monitor/Log Analytics • Understanding of Hadoop ecosystem fundamentals (HDFS, YARN, Hive/Metastore) • Strong grasp of data modeling, file formats (Parquet/ORC/Avro), partitioning, and performance best practices • Experience building production-grade pipelines with testing, monitoring, and alerting • Version control with Git and collaborative development practices • Excellent communication and cross-functional collaboration skills • Understanding of Hadoop ecosystem fundamentals (HDFS, YARN, Hive/Metastore) • Strong grasp of data modeling, file formats (Parquet/ORC/Avro), partitioning, and performance best practices • Experience building production-grade pipelines with testing, monitoring, and alerting • Version control with Git and collaborative development practices • Excellent communication and ability to work cross-functionally

Preferred skill sets:

Stream processing and event-driven architectures (Kafka, Azure Event Hubs, Azure Functions) • Lakehouse technologies (Delta Lake, Unity Catalog) and query engines (Synapse Serverless, Databricks SQL) • Governance and lineage tools (Microsoft Purview, OpenLineage) • Cost optimization on Azure (cluster policies, Photon, spot VMs, storage tiers hot/cool/archive) • Infrastructure as code (Terraform/Bicep) and CI/CD for data workflows (Azure DevOps/GitHub Actions) • Containerization and orchestration (Docker, AKS) and packaging for Databricks (dbx, DABs) • Experience integrating with warehouses (Synapse Dedicated SQL Pools, Snowflake) and BI tools (Power BI) • Security/compliance exposure (PII handling, least-privilege, network isolation)

Years of experience required

5-7 years

Education qualification: