Azure Data Engineer

Accion Labs

8 - 13 years

15 - 25 Lacs

pune bengaluru delhi / ncr

Posted:8 hours ago| Platform:

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Skills Required

adls gen2 azure data factory microsoft fabric pyspark data bricks azure synapse warehouse lakehouse

Work Mode

Hybrid

Job Type

Full Time

Job Description

Role Snapshot

Title:
Senior Microsoft Fabric Data Engineer
Experience:
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

Must-Have Skills (Strong, Hands-On)

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.

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