WNS, part of Capgemini, is an Agentic AI-powered leader in intelligent operations and transformation, serving more than 700 clients across 10 industries, including Banking and Financial Services, Healthcare, Insurance, Shipping and Logistics, and Travel and Hospitality. We bring together deep domain excellence – WNS’ core differentiator – with AI-powered platforms and analytics to help businesses innovate, scale, adapt and build resilience in a world defined by disruption.
Our purpose is clear: to enable lasting business value by designing intelligent, human-led solutions that deliver sustainable outcomes and a differentiated impact. With three global headquarters across four continents, operations in 13 countries, 65 delivery centers and more than 66,000 employees, WNS combines scale, expertise and execution to create meaningful, measurable impact.
Why Join Us?
We promise our employees to experience role clarity, coaching and mentoring, professional development and structured career path through our 5 people promises and keeping employee experience at the core. Experience the culture of Outperformance, engagement, celebration and enjoy wide variety of best in class benefits including free HMO coverage for self and dependent from Day 1; Also contribute to society through our WNS Cares Foundation, where you have the opportunity to support meaningful initiatives and make an impact in the community.
Data Quality Engineer
We are looking for a Data Quality Engineer with 4 to 5 years of experience to build and run data quality controls for an enterprise finance data platform on Azure, Databricks and Snowflake. You will define and automate quality rules, reconciliations and matching checks so that data used for financial control, tax and management reporting can be trusted. Candidates at the upper end of the range may lead a small team of data quality engineers.
KEY RESPONSIBILITIES
- Work with business analysts and data owners to define data quality rules for finance data
- Build automated quality checks in Databricks pipelines for completeness, validity, consistency and timeliness
- Build reconciliations between source systems, the platform and reports, including general ledger to sub-ledger checks
- Build matching rules for bank, call account and intra-group reconciliations
- Set up exception handling and route failed records to data owners for correction
- Build data quality dashboards in Power BI
- Record rule definitions and results in Microsoft Purview
- Investigate recurring data issues and agree fixes with source system owners
- Support testing and production monitoring of quality controls
TECHNICAL SKILLS
Must-have
Databricks, including PySpark and Spark SQL
Advanced SQL
Python
Data quality rule design
Reconciliation and matching logic
Good-to-have
Delta Live Tables expectations
Great Expectations or similar tools
Snowflake
Power BI
Microsoft Purview
DOMAIN KNOWLEDGE
- Finance data, including general ledger, sub-ledgers and intra-group balances
- Balance sheet substantiation and reconciliation controls
- Data quality needs for month-end close, tax and management reporting
CERTIFICATIONS (GOOD TO HAVE)
Databricks Certified Data Engineer Associate
DAMA Certified Data Management Professional (CDMP)
EXPERIENCE AND QUALIFICATIONS
4 to 5 years in data engineering or data quality roles
Built data quality or reconciliation controls on a cloud data platform
Experience with finance or banking data
Degree in engineering or computer applications (B.E., B.Tech or MCA)
Degree in engineering or computer applications (B.E., B.Tech or MCA)
Job description provided by WNS.
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