Senior Data Engineer

HD Supply; Chennai District; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

HD Supply in Chennai (India) is seeking a seasoned Data Engineer to design and implement scalable data pipelines using Azure Data Factory, Databricks, and Synapse. You will build end-to-end data flows, ensure data quality, and enable robust BI capabilities with Power BI in a fast-paced cloud environment.

You will collaborate with data scientists, analysts, and business leaders to align data solutions with strategic goals, while mentoring junior engineers and driving best practices in CI/CD and

Qualifications

• 4-7 years of total experience in data engineering and big data pipeline development.
• Demonstrated experience in Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
• Experience with big data processing frameworks and modern data architecture principles.

Responsibilities

• Design and implement scalable data engineering frameworks.
• Build and maintain ETL/ELT pipelines for data ingestion and transformation.
• Leverage Azure Data Factory, Databricks, and Synapse to deliver enterprise-grade data solutions.
• Optimize Synapse SQL Dedicated Pools to boost BI performance.
• Integrate data pipelines with Power BI.
• Ensure data accuracy through validation, lineage, and quality monitoring.
• Tune workflows for performance and cost-efficiency at scale.
• Collaborate with Data Scientists, Analysts, PMs, and Leaders to align data solutions with goals.
• Share best practices and foster knowledge exchange within the team.
• Mentor junior team members and promote growth.

Skills

SQL

Python

PySpark

ETL/ELT

CI/CD

Git

Data governance

Performance tuning

Data warehousing

Cloud analytics

Tools

Azure Data Factory

Azure Databricks

Azure Synapse Analytics

Databricks

Synapse

Power BI

Job description

Role & responsibilities

Delivery and Execution

• Design and implement scalable, secure, and high-performance data engineering frameworks
• Build and maintain ETL/ELT pipelines to support data ingestion, transformation, and integration
• Leverage Azure tools (Data Factory, Databricks, Synapse) to deliver enterprise-grade data solutions
• Optimize Synapse SQL Dedicated Pools to enhance BI performance
• Integrate data pipelines with reporting platforms like Power BI
• Ensure data accuracy and integrity through validation checks, lineage tracking, and quality monitoring
• Tune workflows for performance and cost-efficiency at scale

Support and Enablement

• Collaborate with Data Scientists, Analysts, Product Managers, and Business Leaders to align data solutions with strategic goals
• Share best practices, drive continuous learning, and foster knowledge exchange within the team
• Gain and apply a deep understanding of FedExs business landscape and customer value drivers
• Support DevOps practices and implement CI/CD pipelines for robust data delivery
• Mentor junior team members and promote a high-performing, growth-oriented team environment

Preferred candidate profile

Required Experience

• 4-7 years of total experience in Data engineering and big data pipeline development
• Demonstrated experience in Azure Data Factory, Azure Databricks, and Azure Synapse Analytics
• Experience with big data processing frameworks and modern data architecture principles

Essential skills

• Advanced proficiency in SQL, Python, and PySpark for scalable data processing and transformation
• Hands-on experience in building end-to-end ETL/ELT pipelines using Azure Data Factory, Synapse, and Databricks
• Strong grasp of data warehousing, lakehouse architecture, and modern analytics on cloud platforms
• Hands-on experience with large-scale data platforms and performance tuning for high-volume data pipelines
• Well-versed in DevOps practices, Git, and CI/CD pipelines for automating data engineering workflows
• Strong understanding of modern data architecture and governance frameworks in a cloud environment

Desired skills

• Proficiency in Power BI and BI tools with experience in building scalable dashboards and optimizing data models
• Ability to independently lead and deliver end-to-end data engineering projects with minimal supervision