Data Engineer (Associate)

Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation, and navigate constant change. We're seeking a Data Engineer to join the Data Science & Machine Learning team in our Commercial Digital practice, where you'll design, build, and optimize the data infrastructure that powers intelligent systems across Financial Services, Manufacturing, Energy & Utilities, and other commercial industries.

This isn't a maintenance role or a ticket queue—you'll own the full data lifecycle from source integration through analytics-ready delivery. You'll 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 real. In your first year, you might architect a lakehouse solution for a global manufacturer's IoT data, build a real-time streaming pipeline for a financial services firm's trading analytics, and design a data mesh implementation for a utility company's 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

• 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 Databricks—designing schemas, optimizing performance, and implementing governance frameworks.
• Ensure data quality and observability—implementing 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.

• 2+ 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 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 architectures—Snowflake, Databricks, and familiarity with open table formats (Delta Lake, Apache Iceberg). We're 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.
• Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field (or equivalent practical experience).
• Willingness to travel approximately 30% to client sites as needed.

• Experience in Financial Services, Manufacturing, or Energy & Utilities industries.
• Background in building data infrastructure for ML/AI systems—feature 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 pipelines—version 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.
• Master's degree in a technical field.

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