Azure Tech Lead

MAIN PURPOSE

The Azure Tech Lead 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, architecture and roadmaps.

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.

Act as a bridge between business group IT, product, and engineering teams to align technical execution with strategic objectives.

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, in Azure's Data Factory and data lake.

Present technical solutions, best practices, and architecture principles to BG IT stakeholders, building alignment across teams.

Design and implement scalable, reusable and efficient data pipelines and streaming solutions to ensure smooth data movement from multiple sources using Azure Databricks

Optimize platform performance for scalability and cost efficiency, and maintain documentation for architecture, processes, and best practices.

Lead business / domain-specific squads, driving agile implementation with a focus on delivery efficiency and continuous improvement.

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.

Manage project timelines, and resources, ensuring timely delivery and adherence to quality standards.

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 vendor engagement for licensing agreements, service contracts, and platform capabilities, liaising on platform issues, and implementing new features and integrations, supporting specialized implementations, performance tuning, and innovation initiatives and driving cost optimization for data platform investments.

Stay up to date with the latest advancements in cloud computing, data engineering, and analytics technologies, and recommend best practices and industry standards for implementing the data lake solutions.

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 : Owns and defines the current and target architecture, lead development and deployment of enterprise-scale data platforms using Azure and Analytics technologies (Data Factory, Databricks), 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 :

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.

Relevant Experience :

10 to 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, Power BI, SAC and other BI, data visualization and exploration tools

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

Experience in data visualization / exploration tools

Will be considered as an advantage but are not required :

Microsoft Certified : Azure Data Engineer Associate

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

Power BI Report development

Exposure to AI services in Azure and Agentic Analytics solutions