Senior Data Engineer (India)

Alimentiv; Bengaluru; Presencial; INR 3 600 000 - 6 000 000; Tempo integral

Resumo da oferta

Alimentiv is seeking a Lead Data Engineer in Bengaluru to design and operationalize scalable data solutions for enterprise analytics and AI initiatives.

You will architect end-to-end data pipelines on Azure Databricks, PySpark, and Azure Data Factory, ensuring robust data modeling and governance across data warehouses and data lakes. You will mentor engineers and partner with stakeholders to align data engineering with business goals.

Qualificações

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field (Master’s preferred).
• 5–8 years of experience designing and developing enterprise-scale data solutions (data warehouses, data lakes, operational databases).
• Expert-level proficiency in Databricks, Azure Fabric, PySpark, SQL, and Azure DevOps.

Responsabilidades

• Architect, design, and implement end-to-end data solutions on Azure Databricks and Azure data services.
• Build and maintain data pipelines from sources to consumption with robust data modeling (bronze-silver-gold).
• Develop automated tests for data jobs and CI/CD pipelines for production readiness.
• Lead technical reviews, mentor junior engineers, and collaborate with business stakeholders.
• Partner with analytics teams to define semantic models and KPIs for self-service analytics.

Conhecimentos

Databricks

PySpark

SQL

Azure

CI/CD

Power BI

Data governance

Python

Formação académica

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

Ferramentas

Azure Databricks

Azure Data Factory

ADLS Gen2

Azure SQL

Azure DevOps

Descrição da oferta de emprego

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.

About the Role

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.

About You

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

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.