Hadoop / PySpark

Infosys; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Infosys in Bengaluru seeks a Senior Lead Data Engineer to drive end-to-end delivery of big data solutions using Hadoop and PySpark, from requirements to production rollout.

You will design scalable ETL pipelines, optimize Spark jobs, mentor engineers, and establish data quality checks and runbooks to support production readiness.

Qualifications

• Bachelor's or Master's degree in Engineering/Technology/Science or equivalent.
• 9–11 years of experience in data engineering and large-scale processing.
• Strong hands-on experience with Hadoop ecosystem and PySpark.

Responsibilities

• Lead end-to-end delivery of big data solutions using Hadoop and PySpark, from requirements to production rollout.
• Design and implement scalable batch/ETL pipelines for large datasets with reliability and performance focus.
• Drive architecture discussions, define standards, and ensure best practices for distributed data processing.
• Mentor engineers through code reviews, design reviews, and knowledge sharing to uplift team capability.
• Establish data quality checks, validation frameworks, and operational monitoring for production pipelines.
• Perform root-cause analysis for pipeline failures and implement preventive fixes.

Skills

Leadership

Stakeholder management

PySpark

Hadoop

Data modeling

Distributed processing

Code reviews

Mentoring

Consulting mindset

Education

BE/BTech/MTech/MCA/MSc or equivalent

Tools

Hadoop

PySpark

Spark

Job description

• Primary skills:Technology->Big Data - Data Processing->PySpark,Technology->Big Data - Hadoop->Hadoop
• Lead end-to-end delivery of big data solutions using Hadoop and PySpark, from requirements to production rollout.
• Design and implement scalable batch/ETL pipelines for large datasets with strong focus on reliability and performance.
• Drive technical architecture discussions, define standards, and ensure best practices for distributed data processing.
• Optimize Spark jobs through partitioning strategies, caching, shuffle tuning, and efficient file formats where applicable.
• Collaborate with stakeholders to translate business needs into technical specifications, delivery plans, and milestones.
• Establish data quality checks, validation frameworks, and operational monitoring for production pipelines.
• Perform root-cause analysis for pipeline failures and performance bottlenecks; implement preventive fixes.
• Mentor engineers through code reviews, design reviews, and knowledge sharing to uplift team capability.
• Ensure documentation, runbooks, and handover artifacts are created and maintained for support readiness.
• Bachelor’s or Master’s degree (or equivalent) in Engineering/Technology/Computer Applications/Science (BE/BTech/MTech/MCA/MSc or equivalent).
• 9–11 years of overall experience in data engineering and large-scale data processing environments.
• Strong hands-on experience with Hadoop ecosystem components and distributed data processing concepts.
• Strong hands-on experience building data pipelines using PySpark.
• Proven ability to lead technical delivery, guide teams, and manage stakeholder expectations in consulting engagements. Preferred Qualifications:
• Experience designing and implementing Spark-based data processing patterns (batch and incremental loads).
• Strong understanding of data modeling and storage patterns for big data platforms (partitioning, compaction, schema evolution).
• Experience with workflow orchestration and scheduling for data pipelines and dependency management.
• Demonstrated expertise in production hardening: monitoring, alerting, SLAs, and incident management for data jobs.
• Strong consulting mindset with ability to present solutions, document designs clearly, and influence technical decisions across teams.