BI Data Engineer II

Boston Beer•Boston, MA

4d•Onsite

About The Position

We are currently hiring a Full-Time BI Data Engineer II in Boston, MA. Join our Boston-based Data and Analytics (DnA) team and play a key role in building and evolving our Enterprise Data Platform with a Databricks-first approach. This position will support the development of scalable Lakehouse data pipelines, curated data assets, and analytics-ready datasets that strengthen business decision-making across the organization. As a hands-on Data Engineer II, you will contribute across the data engineering lifecycle—from requirements gathering and solution design through development, testing, deployment, monitoring, and production support. You will work primarily in Databricks using Spark, Delta Lake, Python, and SQL, while also supporting integrations with our On-Premise SQL data warehouse as we continue modernizing the platform. We are moving toward a domain-oriented team model, and this position will be primarily assigned to one or more business areas to be determined. This gives the engineer the opportunity to build deeper business context while applying modern Databricks patterns such as bronze, silver, and gold layers, reusable transformation logic, data quality controls, and governed data products for enterprise reporting and analytics. This is a strong opportunity for someone with solid data engineering fundamentals who wants to grow deeper in Databricks, Spark, Delta Lake, Python, SQL, and cloud-based data integrations while helping modernize an enterprise data platform. The engineer will help build reliable, production-ready pipelines, improve data quality and governance, and support the transition from legacy SQL-based processes toward a scalable Lakehouse architecture in a collaborative, fast-paced environment.

Requirements

Bachelor’s degree in Computer Science, a closely related field, or equivalent experience.
Databricks & Spark: Hands-on experience developing data pipelines in Databricks using notebooks, jobs, workflows, PySpark or Spark SQL, and Delta Lake.
Programming & SQL: Strong SQL and Python skills, with the ability to write maintainable transformation logic, troubleshoot data issues, and support production pipelines.
Lakehouse Concepts: Working knowledge of Lakehouse architecture, Delta tables, medallion-style layers, batch processing, and analytics-ready data modeling.
Databricks Platform Exposure: Exposure to one or more Databricks platform capabilities such as Unity Catalog, Delta Live Tables, Databricks SQL, job clusters, workflow orchestration, performance tuning, cluster configuration, administration, or resource provisioning.
Data Quality & Governance: Ability to apply data validation, reconciliation, access controls, documentation, and governance practices to support trusted enterprise data products.
CI/CD & Automation: Exposure to source control and automated deployment practices using tools such as Azure DevOps and Databricks Asset Bundles for reliable, production-ready workflows.
Collaboration & Problem-Solving: Ability to work effectively with analysts, stakeholders, and cross-functional teams to troubleshoot and optimize pipelines.

Nice To Haves

AI & Analytics: Exposure to AI, machine learning, feature engineering, or analytics frameworks within a modern data platform is a plus.
Data Modeling & BI: Familiarity with dimensional modeling, semantic layers, and Power BI for building analytics-ready datasets.
ETL/ELT & SQL Server: Experience designing, developing, and supporting relational database pipelines, stored procedures, or SSIS workflows.
Domain & Systems Knowledge: Knowledge of CPG, consumer insights and analytics tools such as Nielsen, supply chain processes, Salesforce, SAP, or other enterprise systems is a plus.
Collaboration & Project Tools: Experience with Jira and Confluence for project tracking, documentation, and team collaboration.
Scripting: PowerShell or automation scripting experience is a plus.

Responsibilities

Design, build, test, and support Databricks Lakehouse data pipelines using Spark, Delta Lake, Python, and SQL for reporting, analytics, and downstream business use cases.
Develop and maintain Databricks notebooks, workflows, jobs, and reusable pipeline components following team standards for version control, documentation, testing, and deployment.
Build and maintain curated datasets and analytics-ready models across Lakehouse layers, including bronze, silver, and gold, with attention to data quality, lineage, and business usability.
Support data ingestion, migration, and integration between Databricks, On-Premise SQL Server, SaaS platforms, and other enterprise systems as part of platform modernization.
Partner with analysts, data scientists, business stakeholders, and teams across the Data Enterprise organization, including Data Operations and MDM, to translate requirements into scalable and maintainable data solutions.
Monitor, troubleshoot, and optimize Spark jobs and Databricks workflows for performance, reliability, and cost efficiency under established engineering best practices.
Implement data validation, error handling, data quality checks, security practices, and governance standards across Databricks.
Maintain, administer, and support the evolution of our Databricks platform as a shared responsibility with other team members.
Support CI/CD and automated deployment practices, including Azure DevOps and Databricks Asset Bundles where applicable, to improve repeatability and production readiness.