Senior Azure Data Engineer Exp - Interviewing Today - Urgent Needs
Accion LabsPune, Maharashtra, India1 day ago
Job descriptionDesign, implement, and deliver batch & streaming data pipelines into Fabric Lakehouse / Warehouse using ADF and Databricks with Delta Lake. Strong grasp of Bronze–Silver–Gold layering, incremental ingestion, watermarking, and best practices for scalable pipelines. Apply Bronze / Silver / Gold patterns, enforce schema evolution, handle late / dirty data, and implement SCD (Type 1 / 2) and late-arriving dimensions. Build solutions on Microsoft Fabric (OneLake, Lakehouse, Warehouse, Pipelines, Dataflows Gen2, Notebooks). Implement security layers : workspace & item permissions, RLS / OLS in Warehouse / Lakehouse SQL endpoints, credentialed connections / shortcuts to external storage, environments & capacities alignment. Create parameterized, template-driven pipelines with reusable activities (ForEach, Lookup, Mapping Data Flows). Ensure robust dependency management with retry / alert patterns. Author complex & nested notebooks (via %run / dbutils.notebook.run) in Python, PySpark, and Spark SQL for ETL / ELT. Debug & troubleshoot jobs and clusters; resolve skew, shuffle spills, checkpoint failures, schema drift, streaming backlogs. Apply performance optimizations : partitioning & clustering, Z-ORDER, OPTIMIZE / VACUUM, file size tuning, AQE, broadcast joins, caching, checkpoint & trigger strategies for Structured Streaming. Implement data quality checks (validations, expectations), idempotency, exactly-once / at-least-once semantics, and dead-letter flows. Set up monitoring & logging (Azure Monitor / Log Analytics, Databricks system tables, Fabric monitoring), with alerting & dashboards. Strong understanding of MS SQL concepts; hands-on experience in writing functions and stored procedures, along with DDL / DML operations. Contribute to data models (star / snowflake), semantic layers, dimensional design, and documentation (solution design docs, runbooks). Implement branching strategy (Git / ADO), perform PR reviews, manage environment promotion (Dev / Test / Prod), and support Fabric CI / CD process. Mentor junior engineers; enforce reusable & scalable patterns. Run client demos and brainstorming discussions. Be self-driven and innovative in solution delivery. Microsoft Fabric (2024+) OneLake, Lakehouse, Warehouse, Pipelines, Dataflows Gen2, Notebooks, capacities, workspace & item security, RLS / OLS. Azure Data Factory (ADF) Reusable, parameterized pipelines; high-level orchestration; robust scheduling, logging, retries, and alerts. Databricks (5+ years on Azure) Python, PySpark, Spark SQL : complex transformations, joins, window functions, UDFs / UDAs. Complex & nested notebooks; modular code with %run / dbutils.notebook.run. Structured Streaming : watermarks, triggers, checkpointing, foreachBatch, schema evolution. Delta Lake : Z-ORDER, OPTIMIZE / VACUUM, MERGE for SCD, Auto Optimize, compaction, time travel. Performance tuning : partitioning, file sizing, broadcast hints, caching, Photon (where available), cluster sizing / pools. Medallion Architecture Bronze / Silver / Gold patterns, SCD (Type 1 / 2), handling late-arriving dimensions. Azure Storage ADLS Gen2 (hierarchical namespace), tiering (Hot / Cool / Archive), lifecycle & cost optimization, shortcuts into OneLake. Data Warehousing Dimensional modeling, fact / aggregate design, query performance tuning in Fabric Warehouse & Lakehouse SQL endpoint. SQL Excellent SQL development; advanced joins, windowing, CTEs, performance tuning / indexing where applicable. Power BI (DAX) Awareness of Power BI and DAX; RLS alignment with Warehouse / Lakehouse. Security & Compliance RBAC, item-level permissions, credentials for data sources, RLS / OLS, secret management (Key Vault), PII handling. ETL / ELT Methodologies Robust, testable pipelines; idempotency; error handling; data quality gates. Ways of Working Agile delivery, client-facing communication, crisp demos, documentation, and best-practice advocacy.
Role Snapshot
- Title : Senior Azure Data Engineer
- Experience : 6–8+ years in Data Engineering (minimum 4+ years on Azure, and ( Or ) 6 months to 1+ year with Microsoft Fabric)
- Tech Focus : Microsoft Fabric, Azure Data Factory (ADF), Databricks (Python, PySpark, Spark SQL), Delta Lake, Power BI (DAX), Azure Storage, Lakehouse, Warehouse
- Engagement : Client-facing, hands-on, design-to-delivery
- Location : Any Accion Labs offices in India - Bangalore, Pune (Preferred), Mumbai, Hyderabad, Indore, Noida - THIS IS A HYBRID Work Model, NOT Remote.
- Notice period : Preferred Immediate joiners or candidates who can join within 20 days are needed
Core Responsibilities
1. End-to-End Engineering
1. Data Architecture Understanding
1. Medallion Architecture
1. Fabric Platform & Security
1. ADF Orchestration & Reusability
1. Databricks Engineering Excellence
1. Data Quality, Observability & Reliability
1. SQL
1. Design, Documentation & Governance
1. CI / CD & ADO Versioning Management
1. Leadership & Client Engagement
Must-Have Skills (Strong, Hands-On)
If interested or know anyone, Kindly write to me at shruti.saboo@accionlabs.com along with your latest CV.
Thank You,
Shruti Saboo
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Azure Data Engineer • Pune, Maharashtra, India
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