Data Engineer

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.

Were seeking a Data Engineer to join the Data Science & Machine Learning team in our Commercial Digital practice where youll design build and optimize the data infrastructure that powers intelligent systems across Financial Services Manufacturing Energy & Utilities and other commercial industries.

This isnt a maintenance role or a ticket queueyoull own the full data lifecycle from source integration through analytics-ready delivery. Youll build pipelines 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 solutions not just write SQL.

The variety is your first year you might architect a lakehouse solution for a global manufacturers IoT data build a real-time streaming pipeline for a financial services firms trading analytics and design a data mesh implementation for a utility companys distribution systems. If you thrive on solving complex data challenges and shipping production systems that ML teams and analysts depend on this role is for you.

What Youll Do

• Design and build end-to-end data pipelines (batch and streaming)from source extraction and ingestion through transformation quality validation and delivery. You own the data infrastructure not just a piece of it.
• Develop modern data transformation layers using dbt implementing modular SQL models testing frameworks documentation and CI/CD practices that ensure data quality and maintainability.
• Build and orchestrate workflows using Microsoft Fabric Apache Airflow Dagster Databricks Workflows or similar tools to automate complex data processing at scale.
• Architect lakehouse solutions using open table formats (Delta Lake Apache Iceberg) on Microsoft Fabric Snowflake and Databricksdesigning schemas optimizing performance and implementing governance frameworks.
• Ensure data quality and observabilityimplementing testing frameworks (dbt tests Great Expectations) monitoring alerting and lineage tracking that maintain trust in data assets.
• Collaborate directly with clients to understand business requirements translate data needs into technical solutions and communicate architecture decisions to both technical and executive audiences.

Required Qualifications

• 2 years (3 years for Senior Associate) of hands-on experience building and deploying data pipelines in productionnot just ad-hoc queries and exports. Youve built ETL/ELT systems that run reliably and scale.
• Strong SQL and Python programming skills with experience in PySpark for distributed data processing. SQL for analytics and data modeling; Python/PySpark for pipeline development and large-scale transformations.
• 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.
• Experience with modern data transformation tools particularly dbt (data build tool). You understand modular SQL development testing and documentation practices.
• Experience with cloud data platforms and lakehouse architecturesSnowflake Databricks and familiarity with open table formats (Delta Lake Apache Iceberg). Were platform-flexible but Microsoft-preferred.
• Familiarity with workflow orchestration tools such as Apache Airflow Dagster Prefect or Microsoft Data Factory. You understand DAGs scheduling and dependency management.
• Solid understanding of data modeling concepts: dimensional modeling data vault normalization/denormalization and knowing when different approaches are appropriate.
• Ability to communicate technical concepts to non-technical stakeholders and work effectively with cross-functional teams including data scientists analysts and business users.
• Bachelors degree in Computer Science Engineering Mathematics or related technical field (or equivalent practical experience).
• Flexibility to work in a hybrid model with periodic travel to client sites as needed.

Preferred Qualifications

• Experience in Financial Services Manufacturing or Energy & Utilities industries.
• Background in building data infrastructure for ML/AI systemsfeature stores (Feast Databricks Feature Store) training data pipelines vector databases for RAG/LLM workloads or model serving architectures.
• Experience with real-time and streaming data architectures using Kafka Spark Streaming Flink or Azure Event Hubs including CDC patterns for data synchronization.
• Familiarity with MCP (Model Context Protocol) or similar standards for AI system data integration.
• Experience with data quality and observability frameworks such as Great Expectations Soda Monte Carlo or dbt tests.
• Experience with high-performance Python data tools such as Polars or DuckDB for efficient data processing.
• Knowledge of data governance cataloging and lineage tools (Unity Catalog Purview Alation or similar).
• Familiarity with DataOps and CI/CD practices for data pipelinesversion control automated testing and deployment automation.
• Cloud certifications (Snowflake SnowPro Databricks Data Engineer Azure Data Engineer or AWS Data Analytics).
• Consulting experience or demonstrated ability to work across multiple domains and adapt quickly to new problem spaces.
• Contributions to open-source data engineering projects or active participation in the dbt/data community.
• Masters degree in a technical field.

Why Huron

Variety that accelerates your growth. In consulting youll work across industries and data architectures that would take a decade to encounter at a single company. Our Commercial segment spans Financial Services Manufacturing Energy & Utilities and moreeach engagement is a new data ecosystem to master and a new platform to ship.

Impact you can measure. Our clients are Fortune 500 companies making significant investments in data infrastructure. The pipelines you build will power real decisionsthe ML models that drive production schedules the dashboards that inform pricing strategies the data products that enable self-service analytics. Youll see your work become the foundation others build on.

A team that builds. Hurons Data Science & Machine Learning team is a close-knit group of practitioners not just advisors. We write code build pipelines and deploy platforms. Youll work alongside engineers and data scientists who understand the craft and push each other to improve.

Investment in your development. We provide resources for continuous learning conference attendance and certification. As our DSML practice grows theres significant opportunity to take on technical leadership and shape our data engineering capabilities.

Position Level

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