Data and Analytics engineering lead

Orbia Pune; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Orbia Pune seeks an Engineering Lead for Data & Analytics Platforms to drive strategy, agile delivery, and platform excellence across enterprise data initiatives on Azure. You will lead end-to-end design, development, and deployment of scalable data pipelines and analytics solutions, mentored by senior engineers and collaborating with BAi, Azure Engineering, and DevOps teams.

The role requires 12+ years IT experience, strong Azure data stack expertise (Databricks, Data Factory, Data Lake, SQL

Qualifications

• 12+ years of IT experience in delivering medium-to-large data engineering, and analytics solutions
• Experience working with Azure Databricks, Azure Data Factory, Azure Data Lake, Azure SQL DW, Azure SQL, Power BI, and other BI, data visualization and exploration tools
• Strong problem-solving skills and the ability to translate business requirements into actionable data science solutions
• Deep understanding of master data management & governance concepts and methodologies
• Experience in Data Modelling & Source System Analysis
• Mastery of SQL and Python for Data Engineering purposes

Responsibilities

• Lead the design, development, and deployment of Azure Data Engineering and Analytics platforms across multiple business groups.
• Be accountable for BG IT engagement, delivery of data and analytics solutions, ensuring alignment with business outcomes.
• Translate business requirements into technical specifications and develop a comprehensive technical plan and oversee execution for the migration, including data ingestion, transformation, storage, and access control.
• Design and implement scalable data pipelines and streaming solutions to ensure smooth data movement from multiple sources using Azure Technologies.
• Establish and ensure data security, quality and integrity throughout the entire data pipeline and analytics platforms, implementing robust data quality, validation and cleansing mechanisms.
• Provide technical guidance and support to the team, resolving any technical challenges or issues that may arise during the migration and post-migration phases and mentoring data engineers and developers.
• Act as a bridge between business group IT, product, and engineering teams to align technical execution with strategic objectives.
• Monitor feedback from stakeholders and drive continuous improvement, innovation initiatives across Data and Analytics technologies.
• Oversee change, incident, and problem management processes, ensuring operational excellence.
• Lead business/domain-specific squads, driving agile implementation with a focus on delivery efficiency and continuous improvement.
• Shape and develop the resourcing pool based on demand, including succession planning, cross-training, and capability building.
• Stay up to date with the latest advancements in cloud computing, data engineering, and analytics technologies, and recommend best practices and industry standards for implementation.

Education

Bachelor’s degree in Computer Sciences/Information Technology or relevant software engineering degree

Tools

Azure Databricks

Azure Data Factory

Azure Data Lake

Azure SQL DW

Azure SQL

Power BI

PySpark

SQL

Python

Databricks Notebooks

Jupyter Notebooks

Job description

The Engineering Lead for Data & Analytics Platforms plays a pivotal role in driving technical strategy, agile delivery, and platform excellence across enterprise data initiatives. This role is accountable for leading the end-to-end design, development, and deployment of scalable data engineering and analytics solutions on Azure—spanning domains such as Supply Chain, Finance, Operations, Customer Experience, HR, Risk Management, and Global IT.

Reporting into cross-functional leadership, the Engineering Lead serves as a strategic partner to Business Analytics & Insights (BAI), Azure Engineering, and DevOps teams. The role ensures alignment of technical execution with business outcomes, oversees platform performance and data governance, and fosters innovation through reusable frameworks and streaming architectures. The ideal candidate will provide hands‑on guidance to engineering squads, mentor data professionals, and champion continuous improvement across data pipelines, security, and analytics platforms.

2. KEY RESPONSIBILITIES

• Lead the design, development, and deployment of Azure Data Engineering and Analytics platforms (Azure Data Factory, Databricks, SAC, Power BI) across multiple business groups.
• Be accountable for Business Group (BG) IT engagement, delivery of data and analytics solutions, ensuring alignment with business outcomes.
• Translate business requirements into technical specifications and develop a comprehensive technical plan and oversee execution for the migration, including data ingestion, transformation, storage, and access control.
• Design and implement scalable and efficient data pipelines and streaming solutions to ensure smooth data movement from multiple sources using Azure Technologies.
• Developing scalable and re‑usable frameworks for ingesting data sets
• Establish and ensure data security, quality and integrity throughout the entire data pipeline and analytics platforms, implementing robust data quality, validation and cleansing mechanisms.
• Optimize platform performance for scalability and cost efficiency, and maintain documentation for architecture, processes, and best practices.
• Provide technical guidance and support to the team, resolving any technical challenges or issues that may arise during the migration and post‑migration phases and mentoring data engineers and developers.
• Act as a bridge between business group IT, product, and engineering teams to align technical execution with strategic objectives.
• Present technical solutions, best practices, and architecture principles to BG IT stakeholders, building alignment across teams.
• Monitor feedback from stakeholders and drive continuous improvement, innovation initiatives across Data and Analytics technologies.
• Oversee change, incident, and problem management processes, ensuring operational excellence.
• Lead business/domain‑specific squads, driving agile implementation with a focus on delivery efficiency and continuous improvement.
• Manage project timelines, and resources, ensuring timely delivery and adherence to quality standards.
• Shape and develop the resourcing pool based on demand, including succession planning, cross‑training, and capability building.
• Stay up to date with the latest advancements in cloud computing, data engineering, and analytics technologies, and recommend best practices and industry standards for implementation.

3. COMPLEXITY OF THE JOB

• Leadership Responsibilities: Leads cross-functional Agile squads and mentors data engineers and developers, ensuring high-quality delivery and technical excellence across multiple business domains.
• Technical Ownership: Oversees the architecture, development, and deployment of enterprise‑scale data platforms using Azure and Analytics technologies (Data Factory, Databricks, SAC, Power BI), ensuring performance, scalability, and security.
• Strategic Alignment: Acts as a key liaison between Business Group IT, product, and engineering teams to align data platform initiatives with strategic business goals and outcomes.
• Cross‑functional Collaboration: Works closely with stakeholders across supply chain, finance, operations, HR, customer experience, and risk management, as well as with DevOps, security, and compliance teams.
• Decision‑making Impact: Makes critical decisions on data architecture, platform optimization, and technology adoption that directly influence business agility, data‑driven decision‑making, and cost efficiency.
• Innovation & Continuous Improvement: Drives innovation through reusable frameworks, streaming architectures, and modern data engineering practices, while continuously improving platform capabilities and delivery processes.
• Operational Excellence: Manages change, incident, and problem processes, ensuring platform reliability, data integrity, and adherence to enterprise standards.
• Resource & Capability Management: Shapes and scales the engineering talent pool through succession planning, cross‑training, and capability development to meet evolving business demands.
• Documentation & Governance: Maintains comprehensive documentation of architecture and best practices, and enforces data governance, quality, and security across the pipeline.
• Geographical Scope: Operates in a global context, coordinating with distributed teams and stakeholders across regions, requiring sensitivity to diverse technical, operational, and cultural environments.

EDUCATION, EXPERIENCE, LANGUAGE, & PHYSICAL REQUIREMENTS :

Academic Level:

Bachelor’s degree in Computer Sciences/Information Technology or relevant software engineering degree.

Languages:

English (fluent); additional languages are a plus.

Knowledge & Experience:

• Providing Technical leadership and guidance to Teams In Data and Analytics engineering solutions and platforms
• Strong problem-solving skills and the ability to translate business requirements into actionable data science solutions.
• Excellent communication skills, with the ability to effectively convey complex ideas to technical and non‑technical stakeholders.
• Strong team player with excellent interpersonal and collaboration skills.
• Ability to manage multiple projects simultaneously and deliver high‑quality results within specified timelines.
• Proven ability to work collaboratively in a global, matrixed environment and engage effectively with global stakeholders across multiple business groups.
• 12+ years of IT experience in delivering medium‑to‑large data engineering, and analytics solutions
• Min. 4 years of Experience working with Azure Databricks, Azure Data Factory, Azure Data Lake, Azure SQL DW, Azure SQL, Power BI, and other BI, data visualization and exploration tools
• Deep understanding of master data management & governance concepts and methodologies
• Experience in Data Modelling & Source System Analysis
• Familiarity with PySpark
• Mastery of SQL
• Experience with Python programming language used for Data Engineering purpose.
• Ability to conduct data profiling, cataloging, and mapping for technical design and construction of technical data flows.
• Preferred but not required -
• Experience in preparing data for Data Science and Machine Learning
• Knowledge of Jupyter Notebooks or Databricks Notebooks for Python development
• Power BI Dataset Development and Dax
• Exposure to AI services in Azure and Agentic Analytics solutions