BRUNT Workwear
DataEngineer
“Data Engineer at BRUNT Workwear. Skills: Data Engineering, BigQuery, Python, SQL. Modernize data transformation logic. Build semantic layers”
What You'll Achieve.
Scale data infrastructure; Harden data infrastructure; Optimize query performance; Reduce compute costs; Guarantee data accuracy; Ensure CCPA compliance; Enable governed, AI-queryable access; Power BRUNT’s next generation of intelligent applications; Ensure gold-tier data yields performant reporting
Industry & Context.
query optimization; performance tuning; cost management; auditing legacy data systems; modernizing legacy data systems
occasional off-hours support for critical pipeline incidents
What They're Looking For.
Must Have
4–7 years of data engineering experience, ownership of production data pipelines, cloud warehouses, ELT/ETL architectures, Google BigQuery, schema design, query optimization, partitioning, clustering, GCP cost management, Advanced SQL skills, Python for API-based ingestion, automation, Fivetran administration, dbt for transformation layer testing, documentation, cloud data governance, PII handling, column-level security, access control implementation, Terraform for infrastructure-as-code, Looker Studio, senior individual contributor, lean environments, inheriting, auditing, and modernizing legacy data systems, Agile/Scrum sprint frameworks, Jira, technical writing skills, mapping data lineage, runbooks, 18+ years old, full-time availability, occasional off-hours support, ability to reliably commute to the office 4x per week
Nice to Have
Retrieval Augmented Generation (RAG) data structures, building semantic layers for LLM or AI agent consumption
What You'll Do.
Modernize data transformation logic
Build semantic layers
Build RAG-compatible data stores
Migrate BigQuery stored procedures to dbt
Support reproducible environment provisioning
Establish Git history practices
Build ELT/ETL pipelines
Maintain ELT/ETL pipelines
Optimize ELT/ETL pipelines
Expand Fivetran coverage
Implement scalable ingestion solutions
Implement table partitioning
Implement incremental load strategies
Implement validation checks
Implement schema contracts
Implement anomaly detection
Lead PII field inventory
Lead column-level security
Maintain data dictionary
Build RAG data stores
Optimize RAG data stores
Ensure gold-tier datasets are performant
Ensure gold-tier datasets are well-structured
Translate business requirements into engineering specifications
Create delivery plans
How You'll Work.
Team & Collaboration
Partner with two data analysts; Partner with full-stack developers; Cross-Functional Support; Partner with data analysts; Work with product team
Communication Scope
technical writing skills
Process & Methodology
Agile sprint cadence, Jira
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