Senior Data Engineer (India)

Alimentiv 2; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Alimentiv 2 is seeking a Lead Data Engineer to design, build, and operationalize scalable data solutions for enterprise analytics and AI/ML initiatives. You will lead end-to-end data pipelines, ensure governance including GxP and GDPR/HIPAA alignment, and drive production readiness with robust testing and automation.

You will architect data models across data lake zones, optimize Spark/SQL workloads, and mentor engineers while collaborating with business stakeholders to deliver value-driven

Qualifications

• Bachelor's degree in CS/IS/Engineering or related field (Master’s preferred).
• 5–8 years of experience designing and developing enterprise-scale data solutions.

Responsibilities

• Architect end-to-end data solutions using Azure Databricks, PySpark, Azure Data Factory, and Azure SQL.
• Design and maintain data pipelines from sources to consumption for specific use cases.

Skills

Data governance

Leadership

Mentoring

Stakeholder engagement

CI/CD

Python/PySpark

SQL

Data modeling

Self-service analytics

Communication

Independent work

Deadlines

Statistical analysis

Education

Bachelor's degree in Computer Science/Information Systems/Engineering

Master’s preferred

Tools

Databricks

Azure Fabric

PySpark

SQL

Azure DevOps

Git

ARM/Bicep/Terraform

Power BI

Tableau

Looker

Azure Data Factory

ADLS Gen2

Azure SQL Server

Data Catalogue

Job description

The Lead Data Engineer will design, build, and operationalize scalable data solutions to support enterprise analytics and AI/ML initiatives. This role requires expert-level proficiency in Databricks, Azure Fabric, PySpark, SQL, and the Azure ecosystem, with deep experience across data warehouses, data lakes, and real-time integration. The Lead Data Engineer will architect end-to-end pipelines using industry-standard tools, drive automation, and move solutions effectively into production. The incumbent will ensure compliance with data governance requirements (including GxP and HIPAA/GDPR) while building reusable, integrated pipelines and analytical models that promote self-service analytics. This role provides technical leadership across the team, mentors junior engineers, and partners with business stakeholders to align data engineering with organizational objectives.

. Data Architecture & Engineering

• Architect, design, and implement end-to-end data solutions using Azure Databricks, PySpark, Azure Data Factory, and Azure SQL.
• Design, build, and maintain data pipelines from data sources through integration to consumption for specific use cases.
• Implement robust data modeling standards across bronze, silver, and gold layers in the data lake.
• Develop data models (conceptual, logical, and/or physical) as required.
• Optimize Spark and SQL workloads for performance, scalability, and cost efficiency.
• Manage metadata using data preparation, integration, and AI-enabled tools and techniques.

. Data Integration & Automation

• Drive automation in data integration; recommend and lead implementation of techniques to automate repeatable data preparation and integration tasks.
• Build API-based integrations (REST/JSON) and real-time ingestion frameworks.
• Automate data workflows using Azure DevOps pipelines and Git-based CI/CD practices.
• Implement parameterized, reusable pipeline templates for ingestion and transformation.
• Develop automated unit, regression, and integration testing frameworks for data jobs.

. Analytics & Data Enablement

• Prepare and curate high-quality datasets for BI, reporting, and advanced analytics.
• Partner with analytics teams using Power BI, Tableau, or similar platforms to define semantic models and KPIs.
• Implement performance-optimized data models for self-service analytics.
• Will occasionally provide support to end users on the use of data visualization solutions.

. Stakeholder Engagement & Leadership

• Lead technical design reviews, mentor junior engineers, and promote best practices.
• Assist cross-functional groups, business analysts, and stakeholders to gather, define, and refine data requirements.
• Collaborate with business and IT stakeholders to align data engineering with organizational objectives.
• Propose innovative data ingestion, preparation, and integration techniques to address stakeholder requirements.
• Contribute to architectural roadmaps and technology evaluations for the data platform.
• In collaboration with functional leaders, identify inefficiencies and recommend improvements to the executive team.

Job Experience & Education Requirements:

Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s preferred)

And

5–8 years of experience designing and developing enterprise-scale data solutions (data warehouses, data lakes, operational databases)

Other:

• Expert-level proficiency in Databricks, Azure Fabric, PySpark, SQL, and Azure DevOps.
• Proven experience with Azure Data Factory, ADLS Gen2, and Azure SQL Server.
• Strong experience with Microsoft Azure data management architectures including Data Warehouse, Data Lake, and Data Catalogue, and supporting processes such as Data Integration, Governance, and Metadata Management.
• Experience with Power BI required; Tableau or Looker a plus.
• Working knowledge of CI/CD automation, version control (Git), and infrastructure as code (ARM, Bicep, or Terraform).
• Experience in life sciences or healthcare industries is a strong plus.
• Good understanding of GxP, GDPR/HIPAA, and applicable CFR/CTR/CTD regulations.
• Demonstrated success working with both IT and business stakeholders while integrating analytics and data science output into business processes and workflows.
• Must have excellent written and verbal communication skills.
• Proven ability to work independently and as part of a team and meet important deadlines.
• Statistical analysis skills are an asset.