Primary skills:Technology->Big Data - Data Processing->Spark Technology->Data Engineering->Databricks Technology->Functional Programming->Scala

Key Responsibilities: Data Engineering & ETL Development
Design, develop, and maintain ETL pipelines using Spark-Scala on Databricks for batch and incremental processing.
Implement data transformations, joins, aggregations, and validations to ensure accurate and consistent outputs.
Build reusable Spark components and follow best practices for modular, maintainable code. Performance, Reliability & Operations
Tune Spark jobs (partitioning, caching, shuffle optimization) to improve performance and cost efficiency.
Monitor job execution, troubleshoot failures, and provide timely production support with root-cause analysis.
Implement logging, error handling, and data quality checks to improve pipeline reliability. Collaboration & Delivery
Work with cross-functional teams to gather requirements and translate them into technical solutions.
Participate in code reviews, documentation, and knowledge sharing to uplift team standards.
Support release cycles by validating outputs, ensuring backward compatibility, and maintaining deployment readiness.

Minimum

Qualifications:




Bachelor’s/Master’s degree (BE/BTech/MSc/MCA/MTech or equivalent).
3–5 years of experience in data engineering or ETL development roles.
Solid hands-on experience with Spark using Scala and working on Databricks.
Solid understanding of ETL concepts, data transformations, and pipeline troubleshooting.
Ability to write clean, testable code and collaborate effectively with technical and non-technical stakeholders.

Preferred

Qualifications:
Experience building end-to-end pipelines on Databricks including notebooks, jobs/workflows, and cluster configuration basics.
Solid SQL skills and experience integrating Spark pipelines with structured data sources and curated datasets.
Familiarity with data quality frameworks, reconciliation strategies, and automated validation checks.
Exposure to CI/CD practices for data engineering (version control, automated testing, release management).
Proven ability to optimize distributed workloads and deliver measurable improvements in runtime and stability.

Positive to have skills: SQL, Delta Lake, Apache Airflow, Azure Data Lake Storage (ADLS), Git

📌 Spark Scala, Databricks Bengaluru (India)
🏢 Infosys
📍 India