Data Engineer Jobs
Data Engineer jobs are open across fintech, healthtech, e-commerce, media, and enterprise software, from entry-level to staff and principal, with specializations in data pipeline architecture, cloud infrastructure, and streaming systems. Find a role that fits from the openings below and apply directly.
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What if you could join a rapidly growing company and play a critical role in bringing new medicines to patients by helping scale the programmable infrastructure behind a new human cellular biology platform?
About Vesalius
Vesalius Therapeutics is pioneering a new platform to generate breakthrough treatments for common diseases. Our platform combines large-scale human genetics, genomics, stem cell studies, high-dimensional cellular phenotyping, and artificial intelligence to uncover causal biology and identify optimal intervention points for new therapies. Vesalius was founded in 2019 by Flagship Pioneering.
Role Overview
We are seeking a Principal Data Engineer to lead the architecture and evolution of the data platform underpinning our human genetics, multi-omics, and high-content imaging programs.
This is a senior, hands-on leadership role with broad technical authority. As the architectural lead for data engineering, you will set the direction for our AWS environment and the pipelines, catalog, and access layers built on it, defining the technical roadmap and the standards others build against. The scope spans core cloud data engineering (orchestration, warehousing, infrastructure as code, cost governance) and the distinctive challenges of scientific data: connecting each microscopy image to its cell line, donor, and study of origin, and enabling scientists to answer genotype questions directly, without specialist intervention.
You will join a focused, high-impact team supporting a research organization whose data generation and analytical ambitions are expanding rapidly. Our platform is established and productive, and is now entering a deliberate modernization phase. You will lead that transition, with significant influence over the platform's next-generation architecture.
At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.
Role Responsabilities
- Serve as technical owner and architect of our AWS environment, including account structure, IAM, networking, compute, and storage.
- Lead and extend cost governance, including budgets, alerting, tagging enforcement, rightsizing, and storage lifecycle and tiering.
- Define and own developer operations for the data platform, including infrastructure as code, CI/CD, container builds, and secrets and environment management.
- Upgrade our data lake, managed orchestration, metadata and data ingestion workflows to scale with future data production and enhance the infrastructure that transfers data from the lab to the cloud.
- Partner with ML and computational biology leaders to define the interface between platform and modelling.
Required Qualifications
- 10+ years building and operating production data infrastructure, with a track record of architecting and owning platforms end to end.
- Deep AWS expertise: S3, IAM, EC2, Batch, Lambda, and Spark (EMR or equivalent). Experience as technical owner of production cloud environments, including multi-account architecture and cross-account access.
- Strong Python and SQL, with a background in production-grade pipeline development.
- Production experience with workflow orchestration (Nextflow, Airflow, Temporal, Step Functions, Redun, or similar) and the judgement to evaluate and select among them.
- Infrastructure as code (CloudFormation/CDK), CI/CD, containers, and observability. DevOps is a core competency for this role.
- Data modelling and warehouse or lakehouse design, including metadata management, lineage, and data quality practice.
- Cost governance at scale, including budgets, tagging, rightsizing, and storage lifecycle across hundreds of terabytes.
- Fluency with scientific data semantics (studies, samples, donors, cell lines, assays) and the ability to partner directly with scientists to translate their questions into data models.
Preferred Qualifications
- Familiarity with genetics and genomics data.
- Microscopy or high-content screening data, including large binary object stores, transfer optimization, and image streaming for model training.
- Databricks, OpenMetadata, or a comparable catalog in production.
- Snowflake, particularly governed access patterns over cloud object storage.
- ML infrastructure: SageMaker or Databricks ML, MLflow or Weights & Biases, feature stores.
- Serving data to LLM or agent clients (MCP servers, tool APIs).
- Experience leading data platform architecture as a senior or principal individual contributor, ideally in biotech, pharma R&D, or a research institute.
We are an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.
We recognize that great candidates often bring unique strengths without fulfilling every qualification. If you have some of the experience listed above but not all, please apply anyway. We are dedicated to building diverse and inclusive teams and look forward to learning more about your background and interest in Flagship.
Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, "FSP") do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.
Privacy Notice for Applicants: When you apply for a role at Flagship Pioneering or one of its portfolio companies, we collect and use personal information you provide (such as your name, contact details, work history, and application materials) to evaluate your application, communicate with you, and comply with legal obligations. Your application data is processed through Greenhouse, our applicant tracking system, and may also be reviewed using AI-assisted screening tools. We do not sell your personal information. California residents have rights under the CCPA/CPRA including to know, delete, and opt out of the sharing of their personal information. If you are located in the EU or UK, we process your data under GDPR and you have rights to access, rectify, and erase your data. To exercise your rights or for questions, contact privacy@flagshippioneering.com.
Data Engineer Jobs by Experience Level
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Who's Hiring
- Amazon173

- Booz Allen Hamilton89

- Deloitte73

- Google54

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Top Industries Hiring
- Technology & Software108
- Consulting & Professional Services19
- Science & Research15
- Banking & Financial Services12
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What Employers Look For
The qualifications that appear most often in data engineer jobs.
- Proficiency in SQL and at least one scripting language such as Python or Scala
- Hands-on experience with cloud data platforms like AWS, GCP, or Azure
- Experience building and maintaining ETL or ELT pipelines in production
- Familiarity with orchestration tools such as Apache Airflow or Prefect
- Bachelor's degree in computer science, engineering, or a related technical field
- Experience with columnar or distributed data warehouses such as Snowflake, BigQuery, or Redshift
Tips for Your Data Engineer Job Search
Tailor your resume to the stack
Data engineer job descriptions vary significantly by stack. Rewrite your resume bullets to mirror the tools listed in each posting, whether that's Spark, dbt, Airflow, or Snowflake, so your experience reads as a direct match rather than a general fit.
Show pipeline work, not just queries
Hiring managers want to see that you build and maintain data infrastructure, not just consume it. Highlight projects where you designed ingestion workflows, handled schema evolution, or reduced pipeline latency, with specific outcomes wherever you can name them.
Apply early to roles that fit
Migrate Mate lists data engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target companies by data maturity stage
A startup building its first warehouse and a Fortune 500 modernizing legacy ETL need very different skills. Read job descriptions for clues about data maturity, such as greenfield builds versus migrations, and lead your cover note with experience that matches that stage.
Prepare a systems design answer for the interview
Most data engineer interviews include at least one open-ended design question, like architecting a batch pipeline or a real-time event stream. Practice walking through trade-offs out loud, covering ingestion, transformation, storage, and orchestration choices, not just the final architecture.
Negotiate with competing offer context
If you hold more than one offer, share that fact during negotiation. Compensation for data engineers varies by specialization and cloud platform, so a competing offer in a similar stack gives you grounded leverage without needing to justify a number from scratch.
Data Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data engineers?
The companies hiring the most data engineers right now include Amazon, Booz Allen Hamilton, and Deloitte, with the largest share of openings in California, Texas, and Virginia, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in technology, financial services, and healthcare, where data infrastructure work tends to be ongoing rather than project-based.
How many data engineer jobs are remote?
About 75% of data engineer openings are fully remote or hybrid as of November 2026, making it one of the more remote-accessible engineering roles in tech. Pipeline and platform engineering work is especially likely to be remote, while roles with heavy cross-functional collaboration or on-premise data infrastructure tend to require more in-office time.
How do you become a data engineer?
Start by building a solid foundation in SQL and Python, then learn how data moves through a pipeline by working with an orchestration tool like Airflow and a cloud warehouse like Snowflake or BigQuery. Build two or three portfolio projects that show end-to-end pipeline work, document them on GitHub, and apply to junior or associate data engineer roles. Many people enter the field from adjacent roles in analytics or software engineering, so prior technical experience accelerates the transition.
Can you get a data engineer job with little experience?
Yes, entry-level data engineer roles exist, and employers often hire candidates who demonstrate practical pipeline skills even without a long employment history. Build portfolio projects that show you can ingest, transform, and serve data reliably, use cloud-free tools like DuckDB or SQLite to keep costs down, and target companies that are still building out their data infrastructure, since those teams are more likely to invest in developing junior engineers.
What does the data engineer interview process look like?
Most data engineer interviews include a recruiter screen, a technical phone interview covering SQL and Python fundamentals, and at least one systems design round where you walk through how you would build or scale a pipeline. Later rounds often involve a take-home or live coding exercise focused on data transformation or pipeline logic. Final rounds typically include a team fit conversation and, at some companies, a review of your past architecture decisions.
Where can I find and apply to data engineer jobs?
You can find and apply to data engineer jobs on Migrate Mate, which lists current openings from employers across the United States. Search the listings to find roles that match your stack and experience level, then apply directly to each one that fits.
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