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Data Engineer - Azure + Fabric

EXL • Business Process Outsourcing (BPO) • AI Consulting & Integration • Bachelor's Degree • Python • SQL • Power BI • ETL/ELT • 2 months ago

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Salary

≈ $15k – $38k per year (Estimated)

Location

In office (Gurgaon)

Seniority

Middle · 4+ years exp

Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Aug 17, 2026. EXL scores B on the Alion truth index.

Overview

Company

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 Engineer - Microsoft Fabric / OneLake

Role Overview

We are looking for a Data Engineer to develop and maintain a modern data platform leveraging Microsoft Fabric, OneLake, and the Medallion Architecture. The role will focus on building scalable data pipelines, implementing data transformation and quality processes, and supporting curated data products for analytics and Power BI.

Key Responsibilities

Develop and maintain Microsoft Fabric / OneLake Lakehouse solutions across Bronze, Silver, and Gold layers.
Build reusable data ingestion pipelines and CDC frameworks for sources including GCP, CSV, Excel, email files, and SharePoint.
Implement Bronze-layer ingestion with audit logging, schema validation, schema-drift detection, and error handling.
Develop Silver-layer transformations for cleansing, standardization, data quality, validation, and exception handling.
Build Gold-layer datasets including curated entities, conformed dimensions, business metrics, KPIs, aggregations, and secure views.
Develop and optimize Spark / PySpark, Python, and SQL workloads for scalable data processing.
Build data pipelines using Fabric Data Pipelines, Synapse Pipelines, and Notebooks.
Support Power BI semantic models and Direct Lake data consumption requirements.
Implement data quality checks, reconciliation processes, monitoring, and operational controls.
Follow data governance and security standards, including Purview, RBAC, PII/PHI controls, classification, and lineage.
Troubleshoot pipeline failures, performance issues, data discrepancies, and production incidents.
Collaborate with Solution Architects, Lead Data Engineers, BI teams, and business stakeholders to deliver scalable data solutions.

Required Experience & Skills

4+ years of overall Data Engineering experience with hands-on experience in Azure/ Microsoft Fabric.
Strong hands-on experience with Microsoft Fabric Lakehouse, OneLake, Warehouse, Data Pipelines, and Notebooks.
Proficiency in Spark / PySpark, Python, SQL, and Delta Lake.
Experience with ETL/ELT, CDC, data ingestion, transformation, and Medallion Architecture.
Experience developing data quality, validation, reconciliation, and exception-handling frameworks.
Knowledge of Power BI semantic models and Direct Lake.
Experience with Microsoft Purview, Azure DevOps, Git, CI/CD, and deployment
Commercial insurance or brokerage data experience preferred.

Education & Certifications

B. Tech / Bachelor’s degree or equivalent in Computer Science, Engineering, Information Technology, or a related field.
Microsoft Fabric or Azure Data Engineer certification preferred.

Key Responsibilities

Develop and maintain Microsoft Fabric / OneLake Lakehouse solutions across Bronze, Silver, and Gold layers.
Build reusable data ingestion pipelines and CDC frameworks for sources including GCP, CSV, Excel, email files, and SharePoint.
Implement Bronze-layer ingestion with audit logging, schema validation, schema-drift detection, and error handling.
Develop Silver-layer transformations for cleansing, standardization, data quality, validation, and exception handling.
Build Gold-layer datasets including curated entities, conformed dimensions, business metrics, KPIs, aggregations, and secure views.
Develop and optimize Spark / PySpark, Python, and SQL workloads for scalable data processing.
Build data pipelines using Fabric Data Pipelines, Synapse Pipelines, and Notebooks.
Support Power BI semantic models and Direct Lake data consumption requirements.
Implement data quality checks, reconciliation processes, monitoring, and operational controls.
Follow data governance and security standards, including Purview, RBAC, PII/PHI controls, classification, and lineage.
Troubleshoot pipeline failures, performance issues, data discrepancies, and production incidents.
Collaborate with Solution Architects, Lead Data Engineers, BI teams, and business stakeholders to deliver scalable data solutions.

Required Experience & Skills

4+ years of overall Data Engineering experience with hands-on experience in Azure/ Microsoft Fabric.
Strong hands-on experience with Microsoft Fabric Lakehouse, OneLake, Warehouse, Data Pipelines, and Notebooks.
Proficiency in Spark / PySpark, Python, SQL, and Delta Lake.
Experience with ETL/ELT, CDC, data ingestion, transformation, and Medallion Architecture.
Experience developing data quality, validation, reconciliation, and exception-handling frameworks.
Knowledge of Power BI semantic models and Direct Lake.
Experience with Microsoft Purview, Azure DevOps, Git, CI/CD, and deployment
Commercial insurance or brokerage data experience preferred.

Education & Certifications

B . Tech / Bachelor’s degree or equivalent in Computer Science, Engineering, Information Technology, or a related field.
Microsoft Fabric or Azure Data Engineer certification preferred.

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