Senior Software Engineer, Data Pipelines

Our mission is to make biology easier to engineer. Ginkgo is constructing, editing, and redesigning the living world in order to answer the globe’s growing challenges in health, energy, food, materials, and more. Our bioengineers make use of an in-house automated foundry for designing and building new organisms.
This role strongly prefers candidates located in the Boston area with the ability to regularly commute to our Seaport headquarters.
Due to the nature of this role, candidates should expect to support U.S. Government customers. Candidates must have the ability to obtain and maintain a U.S. security clearance per business requirements. In connection therewith, candidates must be willing to undergo a background investigation and meet eligibility requirements. Clearance sponsorship will be available.
Candidates who currently hold an active or recently inactive U.S. Government security clearance are encouraged to apply.
Ginkgo Biosecurity is building next-generation biosecurity infrastructure to help governments and partners detect, attribute, and deter biological threats. Our mission extends across public health, national security, and global defense, ensuring nations can rapidly identify dangerous pathogens, understand where threats originate, and respond with confidence.
On our Biosecurity team, you are a software engineer focused on building and operating critical biosecurity data systems. You design reliable data pipelines and models, productionize analytics, and ensure data quality across programs spanning PCR, sequencing, wastewater, biosurveillance, and large-scale environmental monitoring.
This role requires strong software engineering fundamentals—including system design, testing, and code quality—applied to data infrastructure challenges. You will work primarily on backend data systems, designing data warehouses, building ETL/ELT pipelines, and managing data architecture. The role combines platform engineering (e.g., orchestration with Airflow, observability, infrastructure-as-code) with analytics engineering (SQL modeling, testing, documentation) to deliver reliable data products that support threat detection, pathogen attribution, and operational decision-making.
Responsibilities
Data Platform Architecture & Engineering

Plan, architect, test, and deploy data warehouses, data marts, and ETL/ELT pipelines primarily within AWS and Snowflake environments
Build scalable data pipelines capable of handling structured, unstructured, and high-throughput biological data from diverse sources
Develop data models using dbt with rigorous testing, documentation, and stakeholder-aligned semantics to ensure analytics-ready datasets
Data Quality & Governance

Ensure data integrity, consistency, and accessibility across internal and external biosecurity data products
Develop, document, and enforce coding and data modeling standards to improve code quality, maintainability, and system performance
Serve as the in-house data expert, making recommendations on data architecture, pipeline improvements, and best practices; define and adapt data engineering processes to deliver reliable answers to critical biosecurity questions
API & Integration Development

Build high-performance APIs and microservices in Python that enable seamless integration between the biosecurity data platform and user-facing applications
Design backend services that support real-time and batch data access for biosecurity operations
Create data products that empower public health officials, analysts, and partners with actionable biosecurity intelligence
AI & Data Democratization

Democratize access to complex biosecurity datasets using AI and LLMs, making data more discoverable and usable for stakeholders
Apply AI-assisted development tools to accelerate code generation, data modeling, and pipeline development while maintaining high quality standards
Cloud Infrastructure & Performance

Build robust, production-ready data workflows using AWS, Kubernetes, Docker, Airflow, and infrastructure-as-code (Terraform/CloudFormation)
Diagnose system bottlenecks, optimize for cost and speed, and ensure the reliability and fault tolerance of mission-critical data pipelines
Implement observability, monitoring, and alerting to maintain high availability for biosecurity operations
Technical Leadership & Collaboration

Lead data projects from scoping through execution, including design, documentation, and stakeholder communication
Collaborate with technical leads, product managers, scientists, and data analysts to build robust data products and analytics capabilities
Minimum Qualifications

7+ years of professional experience in data or software engineering, with a focus on building production-grade data products and scalable architectures
Expert proficiency with SQL for complex transformations, performance tuning, and query optimization
Strong Python skills for data engineering workflows, including pipeline development, ETL/ELT processes, and data processing; experience with backend frameworks (FastAPI, Flask) for API development; focus on writing modular, testable, and reusable code
Proven experience with dbt for data modeling and transformation, including testing frameworks and documentation practices
Hands-on experience with cloud data warehouses (Snowflake, BigQuery, or Redshift), including performance tuning, security hardening, and managing complex schemas
Experience with workflow orchestration tools (Airflow, Dagster, or equivalent) for production data pipelines, including DAG development, scheduling, monitoring, and troubleshooting
Solid grounding in software engineering fundamentals: system design, version control (Git), CI/CD pipelines, containerization (Docker), and infrastructure-as-code (Terraform, CloudFormation)
Hands-on experience managing AWS resources, including S3, IAM roles/policies, API integrations, and security configurations
Strong ability to analyze large datasets, identify data quality issues, debug pipeline failures, and propose scalable solutions
Excellent communication skills and ability to work cross-functionally with scientists, analysts, and product teams to turn ambiguous requirements into maintainable data products
Preferred Capabilities & Experience

Domain familiarity with biological data (PCR, sequencing, wastewater surveillance, TAT metrics) and experience working with lab, bioinformatics, NGS, or epidemiology teams
Production ownership of Snowflake environments including RBAC, secure authentication patterns, and cost/performance optimization
Experience with observability and monitoring stacks (Grafana, Datadog, or similar) and data quality monitoring (anomaly detection, volume/velocity checks, schema drift detection)
Familiarity with container orchestration platforms (Kubernetes) for managing production workloads
Experience with data ingestion frameworks (Airbyte, Fivetran) or building custom ingestion solutions for external partner data delivery
Familiarity with data cataloging, governance practices, and reference data management to prevent silent data drift
Experience designing datasets for visualization tools (Tableau, Looker, Metabase) with strong understanding of dashboard consumption patterns; familiarity with JavaScript for custom visualizations or front-end dashboard development
Comfort with AI-assisted development tools (GitHub Copilot, Cursor) to accelerate code generation while maintaining quality standards
Startup or fast-paced environment experience with evolving priorities and rapid iteration
Scientific or data-intensive domain experience (life sciences, healthcare, materials science)
The base salary range for this role is $134,300-$189,900. Actual pay within this range will depend on a candidate's skills, expertise, and experience. We also offer company stock awards, a comprehensive benefits package including medical, dental & vision coverage, health spending accounts, voluntary benefits, leave of absence policies, 401(k) program with employer contribution, 8 paid holidays in addition to a full-week winter shutdown and unlimited Paid Time Off policy.
It is the policy of Ginkgo Bioworks to provide equal employment opportunities to all employees, employment applicants, and EOE disability/vet.
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