Specific Accountability :-

• Lead the design, build, and optimization of batch and real-time data pipelines.

• Own data model design, ingestion frameworks, transformation logic, and platform reliability.

• Ensure high standards for data quality, lineage, governance, security, and compliance.

• Drive performance tuning, cost optimization, monitoring, and incident resolution across data

platforms.

• Translate business and analytics needs into scalable technical solutions and delivery plans.

• Guide code reviews, technical standards, and release practices for data engineering work.

• Mentor team members, allocate work, and provide technical leadership on complex initiatives.

• Collaborate with value delivery owners, architects, analysts, scientists, and application teams to

enable trusted data products

Desired Candidate Profile

Minimum Qualification

• Overall 5-7 years of experience in data engineering and transformation on Cloud

• 3+ Years of Very Strong Experience in Azure Data Engineering, Databricks

• Expertise in supporting/developing Data warehouse workloads at enterprise level

• Experience in pyspark is required – developing and deploying the workloads to run on the Spark

distributed computing

• Candidate must possess at least a Graduate or bachelor’s degree in Computer Science/Information

Technology, Engineering (Computer/Telecommunication) or equivalent.

• Cloud deployment: Preferably Microsoft azure

• Experience in implementing the platform and application monitoring using Cloud native tools

• Experience in implement application self-healing solutions through proactive and reactive automated

measures