Data Engineer

Excelra; Hyderabad, Bengaluru, Delhi; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Excelra is seeking a Data Engineer to design, build, and operate scalable data pipelines and curated datasets powering scientific analytics, AI/ML, and RWE assets in Hyderabad.

Work closely with scientists, data scientists, data architects and product owners to ensure data is trustworthy, discoverable, governed, and ready for analytics and enterprise connectivity.

The role emphasizes production-grade ETL/ELT, metadata and lineage, and collaboration across domains to deliver robust data products.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Life Sciences, or related field.
• 5+ years in data engineering or data platform engineering with production pipelines.
• Experience with scientific datasets, clinical/RWE workflows, and governance.

Responsibilities

• Build and operate scalable ingestion and transformation pipelines for diverse datasets.
• Collaborate with scientists, data scientists, and product owners to ensure data quality.
• Implement metadata, lineage, and governance to ensure discoverability and trust.
• Optimize performance, cost, and reliability of data pipelines in production.
• Support analytical environments with data optimized for science and enterprise use.

Skills

SQL

Python

ETL/ELT

Databricks

Data modeling

Data governance

Education

Bachelor’s or Master’s degree in Computer Science / Data Engineering / Life Sciences

Tools

Snowflake

Databricks

Power BI

Job description

Data Engineer

We are seeking a Data Engineer with strong dual competency in data and life science to build

and scale data pipelines and curated datasets that power scientific analytics, AI/ML, and data

products across R&D. You will work in a multidisciplinary environment, partnering closely with

scientists, data scientists, data architects, and product owners to translate scientific workflows

into reliable, reusable, and governed data assets.

This role is hands-on and delivery-focused: you will design, develop, and operate data ingestion

and transformation pipelines, optimize performance and reliability, and ensure data is

discoverable and trusted through strong metadata, lineage, and access controls—within the

scientific data and AI ecosystem for development and RWE assets.

Key Responsibilities

Build & Operate Data Pipelines

Design and implement scalable ingestion and transformation pipelines for diverse

scientific datasets (structured and unstructured), using Databricks as a core execution

environment.

Develop robust ETL/ELT workflows, including batch and (where relevant) incremental

processing, with production-grade practices (testing, monitoring, reliability).

Collaborate with platform and security teams to ensure pipelines follow access,

governance, and operational standards.

Build and maintain data structures and curated layers to support analytics, reporting,

and downstream data products.

Optimize performance and cost through good engineering practices (e.g., efficient

transformations, workload tuning, data lifecycle considerations).

Scientific Context & Business

Work effectively with researchers and scientists to understand workflows and ensure

pipelines preserve scientific meaning, context, and traceability.

Support analytical environments (e.g., Power BI, Tableau) by delivering data in formats and

structures fit for scientific exploration and enterprise connectivity.

Data Quality, Metadata, Lineage & Governance

Embed data quality checks and validation steps into pipelines to ensure data is reliable

and fit for scientific decision-making, and follow data strategy.

Implement and maintain metadata and lineage practices so that datasets are

discoverable and governed (e.g., cataloging, traceability, access controls).

Contribute to governance-by-design by working with architects and governance leads on

standards and reusable patterns.

Collaboration & Delivery

Partner with Data Architects and product teams to implement agreed data models and

patterns in production pipelines.

Support sprint delivery by estimating, implementing, and continuously improving

pipelines and datasets based on feedback and evolving use cases.

Governance, Quality and Alignment

Ensure products comply with data governance, quality, privacy, and compliance

requirements in collaboration with Data Governance and Architecture teams.

Align product decisions with architectural standards and platform capabilities, balancing

short-term needs with long-term sustainability.

Promote reuse and consistency across R&D data products.

Qualifications & Experience

Bachelor’s or Master’s degree (or equivalent experience) in Computer Science, Data

Engineering, Bioinformatics, Biomedical Sciences, Life Sciences, or a related field.

5+ years in data engineering / data platform engineering (or equivalent).

Strong hands-on experience with operating data pipelines in a production environment.

Proven ability to work across scientific and technical domains collaborating credibly with

scientists and technical teams.

Experience working with clinical and RWE datasets and workflows.

Experience with metadata/catalog and lineage practices in enterprise environments.

Required Skills & Competencies

Data pipeline engineering, transformation design, performance optimization.

Expertise with ETL/ELT, collaborative engineering workflows like Snowflake or

Databricks.

Experience with data modelling, data curation.

Experience with data visualization and analytics.

Strong SQL + Python (and comfort supporting scientific tooling needs such as R where

relevant).

Expertise in FAIR data principles, meta data, data catalogues...

Previous experience with semantic, ontology and knowledge graph is a plus.

Experience working in regulated environments (e.g. GxP, data privacy) is a plus