EXL

Analyzes business data and runs outsourced operations

Python Engineer

Data EngineeringConfirmed live in the last 24 hours

Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

Graduate or postgraduate qualification.
Strong Python and pandas expertise, including vectorized, memory-aware, production-grade programming.
Solid SQL fundamentals.
Ability to validate data and handle edge cases involving null joins, leading zeros, data-type drift, precision, encoding, and sort stability.
Experience optimizing performance using profiling and benchmarking tools.
Ability to write reusable, documented, tested code and maintain backward compatibility.
Comfort reading complex data flows, debugging silent data issues, optimizing slow transformations, and shipping maintainable pipeline code.

Responsibilities

Modernize legacy data workflows into production-grade Python pipelines in a cloud-based data environment.
Build reliable, scalable, highly validated data pipelines using structured configurations, logging, Parquet-based processing, and lightweight Streamlit interfaces.
Translate existing workflow logic into clean, vectorized Python while ensuring data accuracy and consistency across large datasets.

Desired Qualifications

Experience with PySpark, Polars, DuckDB, or large-scale data processing.

We’re looking for a strong Python engineer to help modernize legacy data workflows into production-grade Python pipelines running in a cloud-based data environment.

The role focuses on building reliable, scalable, and highly validated data pipelines with structured configs, logging, parquet-based processing, and lightweight Streamlit interfaces. A major part of the work involves translating existing workflow logic into clean, vectorized Python while ensuring data accuracy and consistency across large datasets.

Graduate/ Post Graduate

Strong Python + pandas expertise (vectorized, memory-aware, production-grade)
Solid SQL fundamentals
Data validation & edge-case handling (null joins, leading zeros, dtype drift, precision, encoding, sort stability)
Performance optimization using profiling and benchmarking tools
Clean engineering practices: reusable code, documentation, testing, and backward compatibility

Tech stack includes:

Python,
pandas,
PyArrow/parquet,
SQL,
Google APIs,
Tableau integrations,
Streamlit,
openpyxl, and XML parsing

Experience with PySpark, Polars, DuckDB, or large-scale data processing is a plus.

You should be comfortable reading complex data flows, debugging silent data issues, optimizing slow transformations, and shipping maintainable pipeline code.

About the Company

EXL

Analyzes business data and runs outsourced operations

EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1999

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