{"id":3046704,"url":"https://alion.io/job/exl-big-data-engineer","title":"Big Data Engineer","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":80,"open_postings":232,"ghost_share":0,"stale_share":0.991,"repost_share":0,"time_to_fill_p50_days":9,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":60000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Hadoop","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-07-21T08:29:40Z","employer_posted_date":"2026-07-21","last_verified_at":"2026-10-23T08:29:36Z","board_verified":true,"closed_at":null,"days_open":94,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":93},"description":"Job Description: Lead Data Engineer SummaryWe 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.\nRoles & ResponsibilitiesLead the design, development, and maintenance of robust, scalable data pipelines for ingestion, transformation, and processing of large datasets in an on-premises environment.\nOwn architectural and design decisions for data solutions, evaluating trade-offs and defining technical standards for the team.\nMentor, guide, and support other data engineers through code reviews, design reviews, technical coaching, and hands-on problem-solving.\nBuild and optimize data workflows using Python, Spark, and the Hadoop ecosystem.\nWork extensively with Hadoop ecosystem components (Hive, HDFS, Impala) to manage and query large-scale data.\nManage and optimize batch scheduling and job orchestration using enterprise schedulers such as CA7 or Control-M.\nEnsure data quality, integrity, and performance across data platforms.\nCollaborate with data analysts, data scientists, and business stakeholders to translate data requirements into sound technical designs.\nTroubleshoot and resolve complex issues in data pipelines and production environments, acting as an escalation point for the team.\nChampion best practices for coding standards, version control, testing, and documentation.\nStay current with emerging technologies, particularly AI/ML capabilities, and identify opportunities to apply them to data engineering workflows.\nTechnical 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).\nPython - strong hands-on development experience building production-grade data solutions.\n Big Data / Hadoop ecosystem (Hadoop, Hive, Impala, HDFS) - deep, hands-on experience in on-premises environments.\nApache Spark - solid experience developing and tuning large-scale distributed data processing jobs.\nJob scheduling / orchestration - hands-on experience with CA7 or Control-M (or comparable enterprise schedulers).\nStrong understanding of data structures, ETL processes, and SQL.\nExtensive experience with large-scale data processing and distributed systems.\nDemonstrated ability to make sound architecture/design decisions and to mentor and support other developers.\n Exposure to AI/ML concepts or tools, with a strong willingness to learn and grow in this space.\n Roles & ResponsibilitiesLead the design, development, and maintenance of robust, scalable data pipelines for ingestion, transformation, and processing of large datasets in an on-premises environment.\nOwn architectural and design decisions for data solutions, evaluating trade-offs and defining technical standards for the team.\nMentor, guide, and support other data engineers through code reviews, design reviews, technical coaching, and hands-on problem-solving.\nBuild and optimize data workflows using Python, Spark, and the Hadoop ecosystem.\nWork extensively with Hadoop ecosystem components (Hive, HDFS, Impala) to manage and query large-scale data.\nManage and optimize batch scheduling and job orchestration using enterprise schedulers such as CA7 or Control-M.\nEnsure data quality, integrity, and performance across data platforms.\nCollaborate with data analysts, data scientists, and business stakeholders to translate data requirements into sound technical designs.\nTroubleshoot and resolve complex issues in data pipelines and production environments, acting as an escalation point for the team.\nChampion best practices for coding standards, version control, testing, and documentation.\nStay current with emerging technologies, particularly AI/ML capabilities, and identify opportunities to apply them to data engineering workflows.\n 7-10 years of overall experience in data engineering, with a proven track record in technical leadership (design ownership, mentoring, guiding development 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