Big Data Engineer

EXL Talent Acquisition Team; Pune District; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

EXL Talent Acquisition Team is seeking a Lead Data Engineer in Pune to design and maintain scalable data pipelines in an on-premises Big Data environment. You will own architectural decisions, mentor engineers, and work with cross-functional teams to deliver reliable data solutions.

Strong Python, Spark, and Hadoop experience are required, with exposure to AI/ML concepts. The role emphasizes hands-on leadership, data quality, and collaboration with analysts and business stakeholders to translate

Qualifications

• 7–10 years of data engineering experience with leadership responsibilities.
• Hands-on Python development for production-grade data solutions.
• Deep experience with Hadoop ecosystem in on-premises environments.
• Proficient in Apache Spark for large-scale processing.
• Experience with enterprise schedulers like CA7 or Control-M.
• Strong SQL skills and ETL understanding.
• Ability to mentor and guide other engineers and influence design decisions.
• Exposure to AI/ML concepts with willingness to learn.

Responsibilities

• Lead design, development, and maintenance of scalable data pipelines.
• Own architecture decisions and set technical direction for the team.
• Mentor engineers through code/design reviews and coaching.
• Build and optimize workflows using Python, Spark, and Hadoop tools.
• Collaborate with analysts/scientists to translate data needs into designs.
• Ensure data quality, performance, and reliability across platforms.
• Troubleshoot complex data-pipeline issues in production environments.
• Stay current with AI/ML opportunities applicable to data engineering.

Skills

Python

Big Data

Hadoop

Apache Spark

Control-M

SQL

Mentoring

Architecture

AI/ML

Job description

Job Description: Lead Data Engineer Summary

We are looking for an experienced Lead Data Engineer with 7–10 years of hands‑on experience to design, build, and maintain scalable data pipelines and processing systems in an on‑premises Big Data environment. This is a technical leadership role: beyond strong individual contribution, the ideal candidate will own architecture and design decisions, set technical direction, and mentor and support other developers on the team. The role works closely with cross‑functional teams to deliver reliable, high‑quality data solutions that support business and analytics needs.

Roles & Responsibilities

• Lead the design, development, and maintenance of robust, scalable data pipelines for ingestion, transformation, and processing of large datasets in an on‑premises environment.
• Own architectural and design decisions for data solutions, evaluating trade‑offs and defining technical standards for the team.
• Mentor, guide, and support other data engineers through code reviews, design reviews, technical coaching, and hands‑on problem‑solving.
• Build and optimize data workflows using Python, Spark, and the Hadoop ecosystem.
• Work extensively with Hadoop ecosystem components (Hive, HDFS, Impala) to manage and query large‑scale data.
• Manage and optimize batch scheduling and job orchestration using enterprise schedulers such as CA7 or Control‑M.
• Ensure data quality, integrity, and performance across data platforms.
• Collaborate with data analysts, data scientists, and business stakeholders to translate data requirements into sound technical designs.
• Troubleshoot and resolve complex issues in data pipelines and production environments, acting as an escalation point for the team.
• Champion best practices for coding standards, version control, testing, and documentation.
• Stay current with emerging technologies, particularly AI/ML capabilities, and identify opportunities to apply them to data engineering workflows.

Technical Skills Must Have

• 7–10 years of overall experience in data engineering, with a proven track record in technical leadership (design ownership, mentoring, guiding development teams).
• Python – strong hands‑on development experience building production‑grade data solutions.
• Big Data / Hadoop ecosystem (Hadoop, Hive, Impala, HDFS) – deep, hands‑on experience in on‑premises environments.
• Apache Spark – solid experience developing and tuning large‑scale distributed data processing jobs.
• Job scheduling / orchestration – hands‑on experience with CA7 or Control‑M (or comparable enterprise schedulers).
• Strong understanding of data structures, ETL processes, and SQL.
• Extensive experience with large‑scale data processing and distributed systems.
• Demonstrated ability to make sound architecture/design decisions and to mentor and support other developers.
• Exposure to AI/ML concepts or tools, with a strong willingness to learn and grow in this space.