Key Responsibilities: Data Engineering & Delivery

Lead end-to-end development of data pipelines using ADF for orchestration and Databricks for scalable processing

Design and implement robust ETL/ELT workflows, ensuring data quality, reliability, and maintainability

Develop optimized transformations and jobs using PySpark in Databricks for batch and incremental processing

Build reusable frameworks, templates, and standards for pipeline development and deployment Architecture & Performance

Define solution architecture for ingestion, transformation, and serving layers aligned to platform best practices

Tune Spark jobs for performance and cost efficiency (partitioning, caching, shuffle optimization, file sizing)

Establish monitoring, alerting, and operational runbooks for production pipelines Leadership & Collaboration

Provide technical leadership, code reviews, and mentoring to ensure high engineering standards

Collaborate with stakeholders to translate business requirements into scalable data solutions

Drive delivery planning, estimation, and risk management for data engineering initiatives Minimum Qualifications:

BTECH, MTECH, MCA, MSC (or equivalent) in Computer Science, Engineering, or related field

7â€9 years of experience in data engineering with strong hands-on delivery ownership

Strong expertise in Azure Data Factory (ADF) for pipeline orchestration, scheduling, and integration patterns

Strong expertise in Databricks for building scalable data processing solutions

Hands-on proficiency with PySpark for building and optimizing distributed data transformations

Experience building production-grade pipelines with logging, error handling, and operational support readiness

Preferred Qualifications:

Experience designing medallion/layered data architectures and implementing reusable transformation patterns in Databricks

Strong understanding of data modeling concepts and building curated datasets for analytics consumption

Experience implementing CI/CD practices for data pipelines and notebooks, including automated testing and deployment

Proven ability to lead technical discussions, mentor team members, and drive engineering best practices

Experience improving observability (metrics, alerts, dashboards) and reducing pipeline failures through proactive monitoring

Technology- Big Data - Data Processing- PySpark Technology- Cloud Integration- Azure Data Factory (ADF) Technology- Data Engineering- Databricks

ADF (Azure Data Factory)Databricks+Pyspark