Big Data Engineer

EXL

Lead Data Engineer in charge of developing scalable data systems for an on-premises Big Data environment. Responsible for technical direction, mentoring developers, and collaboration across teams.

Posted 7/21/2026full-timePune • 🇮🇳 IndiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing scalable data pipelines using Python, Spark, and the Hadoop ecosystem, while ensuring data quality and performance. Proven ability to lead technical teams, mentor peers, and make informed architectural decisions in data engineering.

Highest-signal resume keywords

Data Engineering LeadershipPython DevelopmentHadoop Ecosystem ExpertiseApache Spark ProficiencyJob Scheduling/Orchestration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentETL ProcessesSQLData StructuresLarge-Scale Data ProcessingDistributed SystemsTechnical ArchitectureData Quality AssuranceAI/ML ExposureCode Review

Soft Skills

MentoringProblem-SolvingCollaborationTechnical Coaching

Tools & Technologies

HadoopHiveHDFSImpalaApache SparkCA7Control-M

Industry Keywords

Data EngineeringBig DataOn-Premises EnvironmentData WorkflowsData Integrity

Tech Stack

Tools & technologies

ApacheDistributed SystemsETLHadoopHDFSPythonSparkSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development