Assistant Manager - Data Engineering | Quality Role

Company Description

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.

Job Description

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) 

Qualifications

Degree in engineering or computer applications (B.E., B.Tech or MCA)