Role & responsibilities

Lead Data Engineer to design, develop, and maintain data pipelines and ETL workflows for processing large-scale structured and unstructured data. The ideal candidate will have expertise in Azure Data Services (Azure Data Factory, Synapse, Databricks, SQL, SSIS, and Data Lake) along with big data processing, real-time analytics, and cloud data integration and Team Leading Experience.

Key Responsibilities:

1. Data Pipeline Development & ETL/ELT

Design and build scalable data pipelines using Azure Data Factory, Synapse Pipelines ,Databricks, SSIS and ADF Connectors like Salesforce.
Implement ETL/ELT workflows for structured and unstructured data processing.
Optimize data ingestion, transformation, and storage strategies.

2. Cloud Data Architecture & Integration

Develop data integration solutions for ingesting data from multiple sources (APIs, databases, streaming data).
Work with Azure Data Lake, Azure Blob Storage, and Delta Lake for data storage and processing.

3. Database Management & Optimization

Design and maintain cloud data bases (Azure Synapse, BigQuery, Cosmos DB).
Optimize SQL queries and indexing strategies for performance.
Implement data partitioning, compression, and caching for efficiency.

4. Data Governance, Security & Compliance

Ensure data quality, lineage, and governance with tools like Purview.
Implement role-based access control (RBAC), encryption, and security policies.
Ensure compliance with GDPR, HIPAA, and ISO 27001 regulations.

5. Monitoring & Performance Tuning

Use Azure Monitor, Log Analytics, and OpenTelemetry to track performance and troubleshoot data issues.
Automate data pipeline testing and validation.

6. Collaboration & Documentation

Document data models, pipeline architectures, and data workflows.

Immediate joiners are preferred.

Lead Data Engineer-Databricks