Data Engineer (Remote)

• Location: United States
• Workplace: Remote
• Employment Type: Full Time
• Experience: 4–8 years of professional data engineering or backend engineering experience

Core Areas: Data Pipelines, SQL, Python, Data Ingestion, Lakehouse Architecture, Data Quality, API Integrations, ITAR Compliance

Travel: Some travel required

Compensation: $140,000-$200,000 per year

About the Role

This opportunity is for a Data Engineer responsible for connecting data across business platforms, scientific instruments, analytics, and AI systems. The role will build and own production data pipelines for ingestion, cleaning, standardization, storage, and downstream use while developing a cohesive, queryable data platform.

The position combines SQL, Python, modern orchestration, API integrations, lakehouse architecture, data quality, and heterogeneous data processing. A critical part of the work is maintaining secure data boundaries for ITAR export-controlled information through compliant environments, access controls, isolation, and audit trails.

What You'll Do

• Build and maintain reliable, secure connectors across Google Drive, Microsoft GCC High, Slack, and Claude so information can move between core platforms.
• Design data pipelines with compliance boundaries built in, ensuring export-controlled information remains in approved environments with appropriate access controls, isolation, and audit trails.
• Partner with laboratory scientists in the San Diego office to integrate Invert Bio and outputs from analytical chemistry instruments, including HPLC and GC, into a cohesive, queryable data platform.
• Design ingestion, cleaning, and standardization pipelines for heterogeneous real-world data, including instrument exports, spreadsheets, PDFs, and semi-structured laboratory records.
• Build a unified data foundation that supports analytics, dashboards, and machine learning across R&D, manufacturing, and operations.
• Own data pipelines end to end, including schema design, orchestration, monitoring, data quality checks, and documentation.

Qualifications

Required Experience

• 4-8 years of professional data engineering or backend engineering experience.
• Hands-on experience building production data pipelines for batch and/or streaming workloads using modern orchestration tools.
• Experience defining, monitoring, and root-causing data quality problems.
• Experience building reporting and reporting infrastructure, queryable lakehouse architecture, and/or medallion architecture.
• Experience integrating third-party platforms through APIs, including authentication, rate limits, webhooks, and integration edge cases.
• Experience working with heterogeneous data such as instrument files, CSVs, PDFs, and formats not originally designed for integration.

Required Skills

• Strong proficiency in SQL and Python for production data engineering.
• Strong understanding of data ingestion, cleaning, standardization, schema design, orchestration, monitoring, and data quality.
• Ability to design data solutions with security and compliance requirements built in, including access control and data isolation.
• Ability to work with structured, semi-structured, and heterogeneous data sources while maintaining reliable and queryable data pipelines.

Education & Eligibility

• Four-year undergraduate degree in Computer Science or Engineering.
• A science or engineering degree in another field may substitute when paired with 6–10 years of relevant experience.
• This role involves access to data and systems subject to U.S. export control regulations, including ITAR.
• Candidates must be a U.S. citizen or national, lawful permanent resident, or protected individual as defined under 8 U.S.C. § 1324b(a)(3).
• Applicants must be authorized to work for any employer in the United States.

Preferred Qualifications

• Experience with laboratory or scientific data, including LIMS, ELN, or analytical instrument outputs such as HPLC or GC chromatography data.
• Familiarity with government or regulated cloud environments, including Microsoft GCC High and ITAR/CMMC contexts.
• Experience supporting ML/AI workloads, including feature pipelines, retrieval corpora, or data preparation for LLM systems.
• Strong technical writing skills and experience developing technical specifications and standards that software developers can use as data producers and consumers.
• Experience selecting tools and infrastructure and designing the systems on which data pipelines are built.

Benefits

• Medical coverage with a range of plan options, including many options fully covered for employees and dependents at no employee cost.
• 401(k) with an employer match of up to 4% of eligible compensation.
• Paid vacation, company holidays, and an end-of-year shutdown.
• Equity with potential upside as the organization scales.