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Data Engineer

EXL · Business Process Outsourcing (BPO) · AI Consulting & Integration · Python · SQL · ETL/ELT · Azure Data Factory · 11 months ago
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Salary
≈ $19k – $41k per year (Estimated)
Location
In office (Gurgaon)
Seniority
Staff · 8+ years exp

Confirmed on the employer's own hiring board on Nov 1, 2026. First seen by Alion on Sep 23, 2026. EXL scores B on the Alion truth index.

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Impact
Profile match

EXL

EXL, legally ExlService Holdings, is a data analytics, AI and digital operations company headquartered in New York that runs outsourced business processes and builds data and AI solutions for insurers, healthcare organisations, banks, media and retail companies. Founded in 1999 and listed on Nasdaq, it has more than 60,000 employees across six continents, with large delivery centers in Noida, Gurgaon, Pune, Bengaluru and Chennai and a newer AI hub in Dublin. It hires data scientists and data engineers, GenAI and MLOps engineers, analytics managers, solution consultants and client partners, plus talent acquisition, finance and medical coding staff.
exlservice.com · HQ: New York, United States · AI Consulting & Integration · Analytics & BI Consulting · Business Process Outsourcing (BPO) · 5000+ employees · Est. 1999
Tech stack of EXL

Data Engineers design and build data systems and pipelines. Responsibilities include developing data processing workflows, optimizing data storage, and ensuring data accuracy. You will collaborate with data scientists and analysts to meet data requirements and resolve data issues. Strong experience in data engineering and problem-solving skills are required.

Design and implement data pipelines, Optimize data processing and storage, Ensure data solutions meet performance standards, Provide technical support, Collaborate with stakeholders.

8+ years in data engineering/ETL roles, with at least 4+ years in Azure cloud ETL leadership. Azure certifications (e.g., Azure Analytics Specialty, Solutions

Required Qualifications:

  • Azure Data Sources: Azure Data Lake Storage (ADLS), Blob Storage, Azure SQL Database, Synapse Analytics. External Sources: APIs, on-prem databases, flat files (CSV, Parquet, JSON).
  • Tools: Azure Data Factory (ADF) for orchestration, Databricks connectors.
  • Apache Spark: Strong knowledge of Spark (PySpark, Spark SQL) for distributed processing.
  • Data Cleaning & Normalization: Handling nulls, duplicates, schema evolution.
  • Performance Optimization: Partitioning, caching, broadcast joins.
  • Delta Lake: Implementing ACID transactions, time travel, and schema enforcement.
  • Azure Data Factory (ADF): Building pipelines to orchestrate Databricks notebooks.
  • Azure Key Vault: Secure credential management.
  • Azure Monitor & Logging: For ETL job monitoring and alerting.
  • Networking & Security: VNET integration, private endpoints.
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