Data Platform Engineer

Job Title: Data & AI Platform Engineer

Work Location: Bangalore

Work mode: Hybrid

Shift timings: 12:00 PM to 9:00 PM IST

About the role

We are transforming laboratory operations toward a fully digital, data-driven and increasingly intelligent laboratory environment.

The Data & AI Engineer – Digital Laboratory will build and operate the data and automation foundations that enable this transformation.

The primary focus of the role is data engineering: designing reliable data pipelines, integrating laboratory and enterprise systems, building data-lake/lakehouse capabilities, developing analytics-ready data models, and automating data flows into platforms such as Power BI.

At the same time, the role will have exposure to AI, machine learning, intelligent automation and emerging technologies, helping turn laboratory data into actionable insights and new digital capabilities.

This is therefore not a traditional data-engineering role focused only on back-end data fixes. You will work directly on projects aimed at eliminating manual paperwork, reducing unnecessary clicks and repetitive workflows, improving laboratory efficiency, and creating the foundation for AI-enabled laboratory operations.

What you will do

1. Build the Digital Laboratory Data Foundation

Design, develop and maintain data pipelines connecting laboratory, IT and enterprise systems.

Build and evolve data lake/lakehouse architectures for laboratory and operational data.

Develop robust ETL/ELT processes using APIs, databases and other supported integration mechanisms.

Transform raw laboratory data into structured, reusable and analytics-ready datasets.

Develop scalable data models supporting reporting, analytics, automation and future AI/ML applications.

Establish data-quality checks, monitoring, logging and error-handling mechanisms.

Improve data availability, reliability, performance and traceability.

2. Laboratory Data

Work with laboratory systems to enable structured extraction and analysis of relevant laboratory data.

Develop solutions for extracting, transforming and analyzing various laboratory data.

Partner with lab application specialists to understand data structures, workflows and business requirements.

Identify opportunities to convert laboratory data into reusable enterprise data products.

Support investigations of data issues and integration failures in collaboration with app and lab teams.

3. Power BI & Data Automation

Automate data preparation and refresh processes supporting Power BI and management reporting.

Build and maintain reliable datasets, semantic models and data pipelines.

Improve the quality, consistency and usability of laboratory KPIs and management information.

Develop monitoring mechanisms to proactively manage data pipelines and reporting solutions.

4. AI / ML & Intelligent Laboratory

You will not be expected to be an AI researcher. Instead, you will help bring AI and ML into real lab processes.

You will:

Support development of ML use cases such as anomaly detection, prediction and lab performance analysis.

Prepare and engineer datasets required for ML and AI applications.

Work with LLMs, AI agents and RAG-based solutions where they can create practical business value.

Explore opportunities to use AI to reduce manual investigation, reporting and administrative activities.

Help develop intelligent solutions that can identify patterns, exceptions and opportunities for improvement.

Work with the team to move successful AI/ML concepts from experimentation into production.

5. Automation & Digital Workflow Transformation

Work closely with IT, laboratory and business teams to identify:

Paper-based processes that can be digitized.

Repetitive manual data-entry activities.

Unnecessary clicks and system interactions.

Manual reporting and reconciliation activities.

Processes where data must be copied between systems.

Opportunities for API-based integration, workflow automation, RPA or AI-agent-based execution.

The objective is simple:

Make the laboratory work simpler, more digital and more intelligent.

6. Production Support & Continuous Improvement

This is an important part of the role. Once solutions are in production, you will:

Monitor data pipelines, integrations and automated processes.

Troubleshoot data-quality, pipeline and integration failures.

Investigate root causes and implement permanent fixes.

Manage incidents and production issues in collaboration with application owners and vendors.

Maintain technical documentation and operational procedures.

Improve reliability, performance and resilience of existing solutions.

Participate in controlled changes and releases within the regulated environment.

You will therefore have end-to-end ownership from development through production operation and continuous improvement. You will also learn about critical lab apps and support the team when needed.

What we are looking for

Essential

Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, Information Technology, Engineering or a related field.

5–10 years of experience in data engineering, analytics engineering, integration engineering or a closely related discipline.

Strong SQL skills and practical experience working with relational databases.

Strong Python skills for data processing, automation and integration.

Experience designing and maintaining ETL/ELT pipelines.

Experience with cloud data platforms and/or data lake/lakehouse architectures.

Experience with Power BI, including data preparation and data models.

Experience working with REST APIs and system integrations.

Experience with Git and modern software-development practices.

Understanding of data quality, monitoring, logging and production support.

Ability to translate business/process problems into practical technical solutions.

Highly desirable

Experience with Azure data services or equivalent cloud platforms.

Experience with Databricks, Microsoft Fabric, Azure Data Factory or similar technologies.

Experience with laboratory, scientific, manufacturing, healthcare or pharmaceutical data.

Experience with laboratory applications and information systems.

Experience with AI/ML projects or MLOps.

Experience with LLMs, RAG, AI agents or AI APIs.

Experience with Power Automate, RPA or workflow automation.

Knowledge of GMP, CSV or data-integrity principles.

Experience working with regulated environments.

We do not expect you to be an expert in every technology listed above. Strong data-engineering fundamentals and the ability to learn quickly are more important.

What makes this role different

This is not a traditional: “Build data pipelines, fix tickets and maintain dashboards.” role.

You will work at the intersection of: Data Engineering + Digital Laboratory + Automation + AI

with a clear business objective: Build the technology foundation for a fully digital laboratory.

You will see your work directly impact how laboratories operate - from eliminating manual reporting and paperwork to enabling intelligent data analysis, automated workflows and eventually AI-assisted laboratory operations.

You will also have the opportunity to work with emerging technologies including AI agents, machine learning, intelligent automation and modern data platforms, while solving real operational problems rather than building technology for experimentation alone.

Success in this role

Within the first 12–18 months, success could look like:

Reliable automated data pipelines replacing significant manual data preparation.

A scalable laboratory data lake/lakehouse foundation.

Automated Power BI reporting and data refresh processes.

Structured and reusable various data products.

Monitoring and support mechanisms for production data solutions.

Several laboratory processes redesigned to eliminate unnecessary manual steps.

Data foundations supporting new ML/AI use cases.

Reduced manual effort, paperwork, errors and repetitive system interactions.

A growing portfolio of production-grade digital laboratory solutions.

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related Engineering field.