Sr. Data Engineer

Re:Build Manufacturing is seeking a Sr. Data Engineer to operationalize and expand its enterprise Data Lake across on-premises and cloud environments. The role builds and operates data pipelines, models and publishes trusted datasets, and supports data governance, quality, analytics, and AI use cases.

Responsibilities:

• Co-design data interfaces and pipelines in close collaboration with software engineers and technical leads, ensuring alignment with application domain models and product roadmaps
• Build and operate batch, streaming, and change data capture (CDC) pipelines from diverse sources (ERP, CRM, Accounting, knowledge repositories, and other enterprise systems) into the data lake
• Model curated data within the lake into data warehouse structures (e.g., star schemas, wide tables, semantic layers) optimized for business intelligence (BI), ad-hoc analytics, and benchmark reporting
• Publish certified datasets and policy-aware retrieval assets (tables, document embeddings, vector indexes) to enable analytics, AI, and retrieval-augmented generation (RAG) use cases
• Establish robust data observability and quality checks to ensure reliability and consistency
• Apply governance, security, and compliance controls across the data lake and warehouse — including role-based access, encryption, auditing, and data retention — following applicable laws
• Operate the platform reliably by orchestrating jobs, monitoring pipelines, and continuously tuning cost and performance
• Work in accordance with The Re:Build Way, demonstrating collaboration, continuous improvement, and technical excellence in every aspect of data engineering

Requirements:

• 5 - 8+ years of proven experience building production-grade data systems with a strong understanding of cloud-based data lake architectures and data warehouses
• Demonstrated expertise in designing and operating data pipelines (batch, streaming, CDC), including schema evolution, backfills, and performance tuning
• Hands-on proficiency with Python and SQL, including experience with distributed processing frameworks (e.g., Apache Spark) and CI/CD for data workflows
• Proven ability to design and implement ETL/ELT workflows and data modeling techniques (e.g., star schemas, wide tables, semantic models)
• Proficiency with cloud data platforms and services such as AWS, Databricks, and Snowflake, with a focus on scalability and reliability
• Familiarity with open table formats (e.g., Iceberg, Delta, Hudi) and business intelligence data modeling
• Understanding of data governance, lineage, and data quality frameworks to ensure reliability, accuracy, and compliance
• Experience or strong interest in enabling AI/ML use cases (e.g., RAG/search datasets, embeddings, vector indexes)
• Bachelor's degree (BA/BS) in Computer Science, Data Science, Mathematics, Analytics, or a related quantitative field (or equivalent experience)
• Fluency in written and spoken English