Spark-Scala, Databricks

Infosys; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Infosys in Bengaluru is seeking a data engineering professional to design and deliver scalable ETL pipelines using Spark-Scala on Databricks. You will optimize distributed processing, implement data quality checks, and collaborate with data engineers and analysts to meet analytics needs.

The role focuses on building reliable data pipelines, tuning Spark jobs, and supporting production releases while maintaining code quality and scalability across projects.

Qualifications

• Bachelor's/Master's degree in engineering or science.
• 35 years of experience in data engineering or ETL roles.
• Hands-on Spark with Scala on Databricks.
• Strong ETL, data transformations, and pipeline troubleshooting.
• Ability to write clean, testable code and collaborate with stakeholders.

Responsibilities

• 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.
• 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.
• Collaborate 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.

Tools

Spark

Databricks

Scala

SQL

Delta Lake

Apache Airflow

Azure Data Lake Storage

Git

Job description

Job DescriptionJoin a collaborative engineering team where youll turn raw, complex data into trusted insights that power real business decisions. In this role, youll work hands-on with Spark-Scala and Databricks to build scalable data pipelines, optimize distributed processing, and help teams deliver reliable datasets for analytics and downstream applications. Youll partner closely with data engineers, analysts, and stakeholders to understand requirements, translate them into robust ETL solutions, and continuously improve performance and quality. If you enjoy solving data challenges, learning modern cloud data practices, and taking ownership from development through production support, this is a great opportunity to grow your impact while working in a supportive, high-accountability culture.

Roles ResponsibilitiesKey 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.

Technical RequirementPrimary skills:Technology->Big Data - Data Processing->Spark

Technology->Data Engineering->Databricks

Technology->Functional Programming->Scala

Additional ResponsibilityMinimum Qualifications:

• Bachelors/Masters degree (BE/BTech/MSc/MCA/MTech or equivalent).
• 35 years of experience in data engineering or ETL development roles.
• Strong 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.
• Strong 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.

Good to have skills:

• SQL
• Delta Lake
• Apache Airflow
• Azure Data Lake Storage (ADLS)
• Git

Educational RequirementMCA,MSc,MTech,Bachelor of Engineering,BTech

Preferred SkillsTechnology->Big Data - Data Processing->Spark,Technology->Functional Programming->Scala,Technology->Data Engineering->Databricks

Service LineData Analytics Unit