Data Engineer

Responsibilities

• Aid in the creation and maintenance of ELT pipelines that ingest data from ERP, CRM, PLM, QMS, and other enterprise systems.
• Support the evaluation of enterprise data platforms by researching external solutions and documenting findings.
• Assist with data storage tasks, including table format standards, partitioning strategies, and interoperability across the tool ecosystem.
• Establish data quality frameworks, lineage tracking, and pipeline observability, including SLA monitoring, proactive alerting, and production-grade logging.
• Support AI/ML workflows by designing feature pipelines, clean data products, and a governed knowledge layer that keeps data semantically aligned for reliable model and agent consumption.
• Collaborate with application engineers and functional stakeholders to help translate requirements and business logic into technical specifications and data solutions.
• Operate effectively within regulated environments where your pipelines will be subject to compliance and auditability requirements enforced by FDA GMP, ITAR, and DCAA.
• Implement and enforce modern development practices, including source control, peer review, CI/CD pipelines, and automated build/test/deploy workflows (e.g., GitHub Actions).
• Leverage AI-assisted development tooling to accelerate delivery and improve personal productivity.
• Learn and apply layered data architecture patterns to organize data assets for reliability and reuse.

Requirements

• Bachelor's degree in Computer Science, Information Systems, or related fields.
• 3+ years of experience in enterprise integration or data engineering roles (advanced degrees count towards years of experience).
• Experience with cloud data warehouse or Lakehouse platforms (e.g., Snowflake, Databricks).
• Experience with relational (SQL) and non-relational (NoSQL) databases (e.g., PostgreSQL, MySQL, MongoDB, DynamoDB).
• Exposure to data transformation tools (e.g., dbt).
• Proficiency in SQL and at least one general-purpose programming language (e.g., Python).
• Exposure to Enterprise Systems such as MRP, CRM, ERP, etc.
• Foundational understanding of data modeling concepts (star schema, normalization, dimensional modeling).
• Basic proficiency in source control (Git) and basic CI/CD concepts.
• Ability to write clean, documented code and a willingness to learn data quality testing practices.
• Understanding of software architecture principles, data warehousing, and ELT workflows.
• Capability to gather business requirements from stakeholders and take a project from initial concept to finished product.
• Ability to work with end users to rapidly iterate on prototype applications by solving end-user issues.
• Ability to translate technical concepts to non-technical audiences for stakeholders, internal and external to the organization.

Qualifications

• Experience with database administration, including schema design, performance tuning, and query optimization.
• Experience interfacing with engineering and manufacturing groups to understand system designs and translations to data needs.
• Experience supporting AI/ML pipelines, including feature stores, training data pipelines, or model monitoring infrastructure.
• Experience in a start-up or similar high-growth environment.
• Familiarity with workflow orchestration tools (e.g., Airflow).
• Exposure to aerospace, defense, or pharmaceutical data environments.
• Experience working with ERP systems deployed in a design development environment.
• Exposure to Infrastructure as Code for cloud resource provisioning (e.g., Terraform).
• Relevant platform or tooling certifications (e.g., dbt Certified Developer, cloud data platform certifications).