Data Engineer

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Surat 5 Years Exp Posted 26d ago

Key Responsibilities

Data Integration & Consolidation

Migrate and unify on-premises and cloud databases into Azure (e.g., Azure SQL, Azure Storage, Synapse/Microsoft Fabric OneLake).
Design landing, curated, and serving layers aligned to business domains.

Pipeline Engineering (ETL/ELT)

Build and manage pipelines using Azure Data Factory and Microsoft Fabric Data Pipelines/Notebooks; use Synapse/Spark for ingestion, transformation, and orchestration.
Implement CI/CD for data workflows; monitor performance, reliability, and cost.

Data Modeling & Architecture

Develop logical and physical data models (star/snowflake; data vault where appropriate).
Establish standards for schema design, partitioning, and optimization.

Secure Data Exposure

Create well-defined interfaces (views, APIs, lakehouse tables, Semantic Models) for downstream BI/analytics and operational systems.
Implement data quality checks, lineage, and observability.

Governance & Operations

Apply RBAC governance across Azure and Fabric resources to enforce least-privilege access.
Manage metadata/catalog (e.g., Microsoft Purview), tagging, and policies.
Provide operational support during early US hours in addition to Indian business hours.

Requirements

Required Skills & Experience (for complex tasks) – Overall 2.5 - 5 years

Microsoft Azure Data Services (Azure Data Factory, Azure SQL, Synapse/Fabric, Storage):
Microsoft Fabric (Data Pipelines, Lakehouse, Notebooks, OneLake, Semantic Models):
ETL/ELT & Orchestration (ADF pipelines, Fabric pipelines, notebooks, scheduling):
SQL Engineering & Performance Tuning (T-SQL, query optimization, indexing):
Data Modeling & Architecture (dimensional, 3NF, data vault; schema design):
Python or Scala for Data Engineering (Spark, notebooks):
DevOps for Data (Git, CI/CD for pipelines/notebooks; IaC with Bicep/Terraform):
RBAC Governance familiarity (Azure RBAC, Fabric workspace/item permissions, least privilege):
Data Quality & Observability (DQ checks, monitoring, alerting):

Nice to Have

Microsoft Purview for catalog, lineage, and policy management
Power BI (Semantic Models in Fabric, Direct Lake, gateway configuration)
Event-driven ingestion (Kafka/Event Hub/IoT)
- Cost optimization for Fabric/Synapse/Spark workloads