Data / ML Ops Engineer

What We Do

We provide savings on insurance premiums for commercial businesses that are leveraging modern technology on their worksites.

While we began with commercial construction, we're expanding into adjacent sectors, including Energy, Agriculture, and Real Estate.

Our Investors

To date, Shepherd has raised over $20M from leading investors, including:

• Y Combinator – W21 cohort

• Susa Ventures – lead our Pre-Seed round

• Spark Capital - lead our Seed round

• Costanoa Ventures + Intact Ventures – lead our Series A round

• And several more.

Our Team

We're a team of technologists and insurance enthusiasts, bridging the two worlds together. Check out our team page to meet some of us!

The Role

About You & the Role

• You love building data pipelines that power real-world decisions — especially in industries where accuracy and speed matter.
  

• You thrive in fast-moving environments — balancing short-term delivery (get the model live!) with long-term scalability (make the pipeline maintainable).
  

• You’re adaptable and resourceful — when a dataset is messy or an API is flaky, you find creative ways to make it work.
  

• You’ll own data engineering across multiple initiatives — building ingestion pipelines, preprocessing frameworks, and model registry integrations.
  

• You’ll work closely with leadership and technical leads — partnering with actuaries, data scientists, and product managers to make pricing data self-serve and production-ready.
  

What You’ll Do

• Build pipelines and integrations — ingest external data sources and structure them for pricing models.
  

• Develop preprocessing frameworks — ensure parity between training and production datasets across exposures, claims, and firmographics.
  

• Enable model deployment at scale — support the model registries so the actuarial team can push new models into production with minimal engineering lift.
  

• Keep data clean and accessible — design systems that handle structured, semi-structured, and event-based ingestion with speed, accuracy, and transparency.
  

• Collaborate cross-functionally — work with actuaries, engineers, and product managers to define requirements, solve edge cases, and keep models moving from prototype to production.
  

You’d be our dream candidate if…

• You’re a builder at heart — with 4+ years of experience as a Data Engineer, ideally in SaaS, fintech, or insurtech, building pipelines that serve ML/analytics products.
  

• You make things happen — experienced in deploying production-grade ETL, wrangling messy data, and supporting ML model deployment with tools such as Dagster/Airflow/Prefect and data warehouses (Redshift/Databricks/Snowflake), and maybe modeling libraries (statsmodels/scikit-learn/pytorch)
  

• You’re a communicator and collaborator — able to translate technical details (schema migrations, data transformations) into clear decisions with non-technical stakeholders.
  

Bonus points for experience with MLflow/Sagemaker model registries, insurance/actuarial datasets, or prior startup/high-growth environments.

Benefits 

🏥 Premium Healthcare
100% contribution to top-tier health, dental, and vision

🏖️ Unlimited PTO
Flexibility to take the time off, recharge, and perform

🥗 Daily lunches, dinners, and snacks
We work together, and enjoy meals together too

🖥️ SF, NYC, or Dallas-Fort Worth Offices
Premium office spaces on both coasts with daily lunches provided

📚 Professional Development
Access to premium coaching, including leadership development

🏦 401(k) Plan
Competitive 401(k) plan offered

🐶 Dog-friendly office
Plenty of dogs to play with and make friends with in the SF office