Azure Synapse + Data Warehouse - Sr Technical Lead-Data Engg

Birlasoft; Uttar Pradesh; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Birlasoft seeks a Senior Technical Lead for Data Engineering to design, optimize, and troubleshoot enterprise analytics platforms and data pipelines. You will own end-to-end solutions using Azure Synapse Analytics, Azure Data Factory, and Synapse Pipelines, while shaping data lake architectures and high-performance ETL/ELT processes.

The role emphasizes performance tuning, bottleneck identification, and ensuring data quality across analytics ecosystems in a large-scale environment.

Qualifications

• Experience with Azure Synapse Analytics and data platforms.
• Strong knowledge of Azure Data Factory, Synapse Pipelines and ADLS Gen2.
• Expertise in SQL performance tuning and data warehouse architecture.

Responsibilities

• Design, develop, and optimize solutions using Azure Synapse Analytics.
• Build and maintain data integration with Azure Data Factory and Synapse Pipelines.
• Design scalable Data Lake architectures (ADLS Gen2).
• Develop high-performance ETL/ELT frameworks for enterprise data platforms.
• Monitor and troubleshoot large-scale data ingestion, transformation, and processing workloads.
• Identify bottlenecks and optimize data movement and transformation processes.
• Ensure data quality, availability, and reliability across analytics ecosystems.

Skills

Azure Synapse Analytics

Azure Data Factory

ETL/ELT optimization

SQL performance tuning

Data Warehouse architecture

Synapse Pipelines

Tools

ADF (Azure Data Factory)

Synapse Pipelines

ADLS Gen2

Job description

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Location: INDIA - NOIDA- BIRLASOFT OFFICE

Title: Azure Synapse + Data Warehouse - Sr Technical Lead-Data Engg

Description

Area(s) of responsibility

We are seeking an experienced Azure Synapse Performance Engineer with strong expertise in Azure Synapse Analytics, Azure Data Factory, ETL/ELT optimization, SQL performance tuning, and Data Warehouse architecture. The ideal candidate will be responsible for designing, optimizing, and troubleshooting enterprise-scale analytics platforms, data pipelines, and data warehouse workloads while ensuring high performance, scalability, and reliability. Similar responsibilities are referenced in internal documentation related to Azure Synapse, Data Lakes, ETL optimization, pipeline orchestration, and performance tuning.

Key Responsibilities

Azure Synapse & Data Engineering

• Design, develop, and optimize solutions using Azure Synapse Analytics.
• Build and maintain data integration solutions using Azure Data Factory (ADF) and Synapse Pipelines.
• Design and implement scalable Data Lake architectures (ADLS Gen2).
• Develop high-performance ETL/ELT frameworks for enterprise data platforms.
• Monitor and troubleshoot large-scale data ingestion, transformation, and processing workloads.
• Identify performance bottlenecks and implement optimization strategies across data movement and transformation processes.
• Ensure data quality, availability, and reliability across analytics ecosystems. These responsibilities align with internally referenced practices around data warehousing, ETL optimization, data lakes, and Synapse performance tuning.

SQL & Data Warehouse Performance

• Perform advanced SQL query analysis and optimization.
• Analyze and optimize execution plans for complex analytical workloads.
• Configure and tune Synapse Dedicated SQL Pools.
• Design and implement efficient partitioning strategies for large datasets.
• Optimize indexing, statistics, and workload distribution.
• Perform capacity planning and performance benchmarking.
• Support data warehouse modernization and optimization initiatives.

Required Technical Skills

Azure Technologies

• Azure Synapse Analytics
• Azure Data Factory (ADF)
• Synapse Pipelines
• Azure Data Lake Storage (ADLS Gen2)
• Azure SQL Database
• Azure Monitoring and Log Analytics

Data Engineering

• ETL/ELT Design and Development
• Data Lake Architecture
• Data Integration and Orchestration
• Batch and Incremental Data ProcessingPerformance Tuning and Optimization

SQL & Data Warehousing

• Advanced T-SQL
• Query Performance Tuning
• Execution Plan Analysis
• Data Modeling (Star Schema / Snowflake Schema)
• Partitioning and Indexing Strategies
• Statistics Management
• Synapse SQL Pools
• Data Warehouse Optimization

Performance Engineering

• Root Cause Analysis
• Workload Monitoring
• Troubleshooting Large-Scale Data Processing
• Capacity Planning
• Performance Benchmarking