Lead Software Engineer, Data Engineering
ValGenesis · Chennai
- Experience6–12 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelsenior
- Posted22 Sept 2026
About ValGenesis
ValGenesis is hiring in Chennai in pharma biotech. This role looks for around 6+ years of experience.
Skills
- Python
- SQL
- C#
- Java
- Scala
- SQL Server
- PostgreSQL
- MySQL
- MongoDB
- Cosmos DB
- Azure Data Lake
- Databricks
- Azure Data Factory
- Apache Airflow
- dbt
- Kafka
- Event Hubs
- Service Bus
- TensorFlow
- PyTorch
- Power BI
- Apache Superset
- Tableau
- Azure
- Docker
- Kubernetes
- GitHub Actions
- Azure DevOps
- data modeling
- version control
- CI/CD
- Azure Synapse
- Delta Lake
- Apache Spark
- Azure ML
The role
A data engineer at a life sciences digital platform company designs data ingestion, lakehouse architecture, and machine learning pipelines using Azure Synapse, Databricks, and Apache Spark, while enabling compliant analytics and production data services with Azure ML and Kubernetes.
Full job description
About ValGenesis
ValGenesis is a leading digital validation platform provider for life sciences companies. ValGenesis suite of products are used by 30 of the top 50 global pharmaceutical and biotech companies to achieve digital transformation, total compliance and manufacturing excellence/intelligence across their product lifecycle.
Learn more about working for ValGenesis, the de facto standard for paperless validation in Life Sciences: https://www.valgenesis.com/about
About the Role:
Responsibilities
Architect, Design, develop, and maintain data ingestion, transformation, and orchestration pipelines (batch and real-time)Build and optimize data Lakehouse architectures using Azure Synapse, Delta Lake, or similar frameworksIntegrate and manage structured and unstructured data sources (SQL/NoSQL, files, documents, IoT streams)Develop and operationalize ETL/ELT pipelines using Azure Data Factory, Databricks, or Apache SparkCollaborate with Data Scientists to prepare and serve ML-ready datasets for model training and inferenceImplement data quality, lineage, and governance frameworks across pipelines and storage layersWork with BI tools (Power BI, Superset, Tableau) to enable self-service analytics for business teamsDeploy and maintain data APIs and ML models in production using Azure ML, Kubernetes, and CI/CD pipelinesEnsure scalability, performance, and observability of data workflows through effective monitoring and automationCollaborate cross-functionally with engineering, product, and business teams to translate insights into actionMentor junior team members and review their work
Requirements
Experience: 6 to 12 years in Data Engineering or equivalent rolesProgramming: Strong in Python, SQL, and at least one compiled language (C#, Java, or Scala)Databases: Experience with relational (SQL Server, PostgreSQL, MySQL) and NoSQL (MongoDB, Cosmos DB) systemsData Platforms: Hands-on experience with Azure Data Lake, DatabricksETL/ELT Tools: Azure Data Factory, Apache Airflow, or dbtMessaging & Streaming: Kafka, Event Hubs, or Service Bus for real-time data processingAI/ML Exposure: Familiarity with ML frameworks (TensorFlow, PyTorch) and MLOps conceptsVisualization & Analytics: Power BI, Apache Superset, or TableauCloud & DevOps: Azure, Docker, Kubernetes, GitHub Actions/Azure DevOpsBest Practices: Solid understanding of data modeling, version control, and CI/CD for data systems
Preferred Skills
Preferred Skills (Nice to Have):Experience in knowledge graph or semantic search solutionsUnderstanding of LLM-based data retrieval (RAG) patternsExposure to data mesh, data fabric, or domain-oriented data architectureFamiliarity with MLflow, Delta Live Tables, or Data Bricks Unity Catalog
Soft Skills
Strong analytical and problem-solving abilityExcellent communication and collaboration skillsAttention to detail and ability to work with large, complex datasetsCreativity and ability to automate repetitive workflowsPassion for continuous learning and innovation in data and AI technologies
We’re on a Mission
In 2005, we disrupted the life sciences industry by introducing the world’s first digital validation lifecycle management system. ValGenesis VLMS® revolutionized compliance-based corporate validation activities and has remained the industry standard.
Today, we continue to push the boundaries of innovation ― enhancing and expanding our portfolio beyond validation with an end-to-end digital transformation platform. We combine our purpose-built systems with world-class consulting services to help every facet of GxP meet evolving regulations and quality expectations.
The Team You’ll Join
Our customers’ success is our success. We keep the customer experience centered in our decisions, from product to marketing to sales to services to support. Life sciences companies exist to improve humanity’s quality of life, and we honor that mission.
We work together. We communicate openly, support each other without reservation, and never hesitate to wear multiple hats to get the job done.
We think big. Innovation is the heart of ValGenesis. That spirit drives product development as well as personal growth. We never stop aiming upward.
We’re in it to win it. We’re on a path to becoming the number one intelligent validation platform in the market, and we won’t settle for anything less than being a market leader.
How We Work
Our Chennai, Hyderabad and Bangalore offices are onsite, 5 days per week. We believe that in-person interaction and collaboration fosters creativity, and a sense of community, and is critical to our future success as a company.
ValGenesis is an equal-opportunity employer that makes employment decisions on the basis of merit. Our goal is to have the best-qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristics protected by local law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.