- Databricks certification (Data Engineer Professional or equivalent demonstrated depth).
- Unity Catalog at multi-team scale: catalogs, schemas, external locations, permissions, and lineage.
- dbt at scale on Databricks, and Python-based modeling frameworks over Delta Lake.
- Dagster and Dagster Cloud, including assets, asset checks, and branch deployments.
- Practical experience with data contracts, ODCS, or data-mesh style data product ownership.
- Experience with a declarative Python ingestion framework such as dlt (dltHub) or comparable.
- Data quality and observability tooling such as Great Expectations, Monte Carlo, or similar.
- Familiarity with MLOps practice – MLflow, model registries, and model serving — sufficient to design data products that ML systems can depend on.
- Azure and Azure DevOps.
- Financial services, insurance, or another regulated industry, including data access, lineage, and audit expectations.
- Experience introducing AI-assisted development into a team’s normal workflow in a disciplined way.
Employee Benefits
We aim to protect the wellbeing of our employees and their families with a broad benefits offering. In addition to offering comprehensive health, dental and vision insurance, we support emotional wellbeing through mental health benefits and an employee assistance program. Work/life balance is important and Protective offers a variety of paid time away benefits (e.g., paid time off, paid parental leave, short-term disability, and a cultural observance day).
The financial health of our employees is just as important as physical and emotional health. Some of the financial wellbeing benefits include contributions to healthcare accounts, a pension plan, and a 401(k) plan with Company matching. All employees are encouraged to protect their overall wellbeing by engaging in ProHealth Rewards, Protective’s platform to improve wellbeing while earning cash rewards.
Eligibility for certain benefits may vary by position in accordance with the terms of the Company’s benefit plans.