Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.
Join our team as the expert you are now and create your future.
Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation, and navigate constant change. We're seeking a Data Engineering Manager to join the Data Science & Machine Learning team in our Commercial Digital practice, where you'll lead the design, development, and delivery of data infrastructure that powers intelligent systems across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries.
Managers play a vibrant, integral role at Huron. Their invaluable knowledge reflects in the projects they manage and the teams they lead. Known for building long-standing partnerships with clients, they collaborate with colleagues to solve their most important challenges. Our Managers also spend significant time mentoring junior staff on the engagement team—sharing expertise, feedback, and encouragement. This promotes a culture of respect, unity, collaboration, and personal achievement.
This isn't a maintenance role or a ticket queue—you'll own the full data lifecycle from source integration through analytics-ready delivery, while leading and developing a team of data engineers. You'll build systems that matter: real-time data architectures that feed mission-critical ML models, transformation layers that turn messy enterprise data into trusted datasets, and orchestration systems that ensure reliability at scale. Our clients are Fortune 500 companies looking for partners who can engineer and lead, not just advise.
The variety is real. In your first year, you might lead a lakehouse implementation for a global manufacturer's IoT data, oversee a real-time streaming architecture for a financial services firm's trading analytics, and architect a data mesh strategy for a utility company's distribution systems—all while developing the next generation of data engineering talent at Huron. If you thrive on solving complex data challenges, shipping production systems, and building high-performing teams, this role is for you.
Requirements:
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9+ years of hands-on experience building and deploying data pipelines in production—not just ad-hoc queries and exports. You've built ETL/ELT systems that run reliably, scale, and are maintained over time.
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Experience leading and developing technical teams—including coaching, mentorship, code review, and performance management. Demonstrated ability to build high-performing teams and develop junior talent.
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Strong SQL and Python programming skills with deep experience in PySpark for distributed data processing. SQL for analytics and data modeling; Python/PySpark for pipeline development and large-scale transformations.
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Experience building data pipelines that serve AI/ML systems, including feature engineering workflows, vector embeddings for retrieval-augmented generation (RAG), and data quality frameworks that ensure model reproducibility. Familiarity with emerging agent integration standards such as MCP (Model Context Protocol) and A2A (Agent-to-Agent), and the ability to design data services and APIs that can be discovered and consumed by autonomous AI agents.
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Experience with modern data transformation tools, dbt particularly. You understand modular SQL development, testing, documentation practices, and how to implement these at scale across teams.
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Experience with cloud data platforms and lakehouse architectures—Snowflake, Databricks, Microsoft Fabric, and familiarity with open table formats (Delta Lake, Apache Iceberg). We're platform-flexible but Microsoft-preferred.
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Proficiency with workflow orchestration tools such as Apache Airflow, Dagster, Prefect, or Microsoft Data Factory. You understand DAGs, scheduling, dependency management, and how to design reliable orchestration at scale.
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Solid foundation in data modeling concepts: dimensional modeling, data vault, normalization/denormalization, and understanding of when different approaches are appropriate for different use cases.
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Excellent communication and client management skills—ability to communicate technical concepts to non-technical stakeholders, lead client meetings, and build trusted relationships with executive audiences.
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Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field (or equivalent practical experience).
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Willingness to travel approximately 30% to client sites as needed.
Manager
India
