About the role

Data Engineer with MDM

Location:	India
Type:	Full-time
Experience:	6-8 years
Functions:	Consulting, Finance, Information Technology, Master Data Management
Industries:	Capital Markets, Investment Banking, Alternative Investments, Financial Services

Job Description

We are looking for a Python & PySpark developer and data engineer who can design and build solutions for one of our Fortune 500 Client programs in the realm of Financial Master & Reference Data Management. This is high visibility, fast-paced key initiative will integrate data across internal and external sources, provide analytical insights, and integrate with the customer’s critical systems.

Key Responsibilities

Ability to design, build and unit test applications on Spark framework on Python.
Build Python and PySpark based applications based on data in both Relational (Oracle, SQL Server) and NoSQL database e.g. Redis, Valkey.
Build ingestion pipelines to load data into in-memory caching database.
Build data pipelines to load and export data to/from the Master Data Management or Reference Data Management Platform using PySpark.
Hands on experience doing at least one MDM implementation using TIBCO, Informatica, GoldenSource or custom-built MDM/RDM platforms.
Experience with designing/building Data APIs and its interaction with data consumers.
Optimize performance of the built Spark applications using configurations around Spark Context, Spark-SQL and Data Frame.
Build Python programs using module libs e.g. pandas, requests, json, flask, pickle.
Experience in processing large amounts of structured data, including integrating data from multiple sources.
Ability to design & build real-time applications using REST API, JSON, XML.
Ability to build solutions on AWS services using Glue ETL, Lambda functions on Python.
Create and maintain integration and regression testing framework on Jenkins integrated with BitBucket and/or GIT repositories.
Participate in the agile development process, and document and communicate issues and bugs relative to data standards in scrum meetings.
Work collaboratively with onsite and offshore team.
Develop & review technical documentation for artifacts delivered.
Ability to solve complex data-driven scenarios and triage towards defects and production issues.
Participate in code release and production deployment.
Challenge and inspire team members to achieve business results in a fast paced and quickly changing environment.

Requirements

BE/B.Tech/ B.Sc. in Computer Science/ Statistics, Econometrics from an accredited college or university.
Minimum 4 years of extensive experience in design, build and deployment of PySpark-based applications.
Hands-on experience in Redis, Valkey, OpenSearch database.
In-depth knowledge of Python core programming language – lists, dictionaries, tuples.
Expertise on Python libraries e.g. pandas, requests, json, flask, pickle.
Good understanding on Informatica PowerCenter for building data pipelines.
Understanding of Master Data Management processes & domain is preferred.
Hands-on experience writing complex SQL queries, exporting, and importing large amounts of data using utilities.
Ability to build abstracted, modularized reusable code components.
Hands-on experience in generating/parsing XML, JSON documents, and REST API request/responses.
Able to quickly adapt and learn.
Able to jump into an ambiguous situation and take the lead on resolution.
Able to communicate and coordinate across various teams.
Are comfortable tackling new challenges and new ways of working.
Are ready to move from traditional methods and adapt into agile ones.
Comfortable challenging your peers and leadership team.
Can prove yourself quickly and decisively.
Excellent communication skills and Good Customer Centricity.
Strong Target & High Solution Orientation.

About EXL

EXL company is from our beginnings in business process services to becoming a global leader in data and AI, EXL brings 25+ years of proven expertise helping enterprises transform and redefining what's possible for our clients.

We partner with leading companies in insurance, healthcare, banking and capital markets, retail, media and communications, and energy and infrastructure to reimagine business models, deliver measurable outcomes, and accelerate innovation. While nearly 70% of enterprise AI initiatives fail, EXL consistently delivers a 90% success rate by integrating deep industry knowledge, robust data and analytics capabilities, and cutting-edge AI implementation in client workflows, turning complexity into value at scale.

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