Data Engineer

BRUNT Workwear•North Reading, MA

12d•$120,000 - $140,000•Hybrid

About The Position

BRUNT is at a pivotal inflection point, transitioning its fast-scaling data environment from a functional BigQuery warehouse into a governed, AI-ready data platform. This role is seeking a hands-on Data Engineer to serve as the principal engineering resource, bringing the discipline, documentation rigor, and modern tooling needed to harden and scale the data infrastructure for the company's next phase of growth. The Data Engineer will report to the SVP of Technology within the DevOps team and will partner closely with two data analysts and full-stack developers. This role will take ownership of the data environment, modernize existing transformation logic, and build semantic layers and RAG-compatible data stores to power BRUNT’s next generation of intelligent, AI-driven applications.

Requirements

4–7 years of data engineering experience with demonstrated ownership of production data pipelines, cloud warehouses, and ELT/ETL architectures.
Strong hands-on proficiency with Google BigQuery, including schema design, query optimization, partitioning, clustering, and GCP cost management.
Advanced SQL skills (ability to audit and refactor complex legacy stored procedures) paired with proficiency in Python for API-based ingestion and automation.
Deep experience administering managed ingestion platforms like Fivetran and a strong working knowledge of dbt for transformation layer testing and documentation.
Working knowledge of cloud data governance, including PII handling, column-level security, and access control implementation.
Familiarity with Terraform for infrastructure-as-code and Looker Studio (or similar BI tools) to ensure gold-tier data yields performant reporting.
A proven track record of operating as a senior individual contributor in lean environments, with a knack for inheriting, auditing, and modernizing legacy data systems.
Experience working within Agile/Scrum sprint frameworks using Jira, alongside strong technical writing skills for mapping data lineage and runbooks.
Must be 18+ with full-time availability (including occasional off-hours support for critical pipeline incidents) and the ability to reliably commute to the office 4x per week.

Nice To Haves

Familiarity with Retrieval Augmented Generation (RAG) data structures and experience building semantic layers for LLM or AI agent consumption.

Responsibilities

Lead the knowledge transfer and stabilization of existing BigQuery stored procedures, with a strategic vision to migrate them to dbt over time.
Utilize tools like Terraform to support reproducible, version-controlled environment provisioning across GCP projects.
Establish strict Git history and peer visibility practices so no critical data logic exists without documented context.
Build, maintain, and optimize robust data flows from critical source systems into Google BigQuery.
Own the health and optimization of 40+ source datasets, managing sync schedules and expanding coverage as new systems onboard.
Design and implement scalable ingestion solutions for non-Fivetran data sources, including structured replacements for file-based ingestion methods.
Implement table partitioning, clustering, and incremental load strategies across high-volume tables to optimize query performance and reduce compute costs.
Implement validation checks, schema contracts, and anomaly detection to guarantee the accuracy of data reaching the gold tier.
Lead the PII field inventory and column-level security access controls to ensure strict CCPA compliance.
Maintain a centralized data dictionary and field-level definitions that serve both technical and non-technical audiences.
Partner with Full Stack Developers to build a semantic layer over BigQuery, enabling governed, AI-queryable access to data domains.
Build and optimize data stores for Retrieval Augmented Generation (RAG) to power BRUNT’s next generation of intelligent applications.
Partner with data analysts to ensure gold-tier datasets are performant and well-structured for Looker Studio dashboards, managing workflow via Jira within an Agile sprint cadence.
Serve as the technical data engineering point of contact within the DevOps team, translating business-driven data requirements into engineering specifications and delivery plans.