Senior Data Engineer
New
P
Pie Insurance - ContractsCommercial Insurance
São Paulo, Brazil (Remote); based in BrazilContractSenior
Salary not disclosed
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Job Details
- Experience
- Minimum 5 years experience
- Required Skills
- PythonSQLSnowflakeAirflowCI/CDData modeling
Requirements
- Minimum 5 years experience as a software engineer or data engineer with a focus on data systems.
- Advanced proficiency writing complex SQL and manipulating large structured and semi-structured datasets.
- Proficiency in Python for building production-grade data pipelines.
- Hands-on Snowflake administration experience, including warehouse management, RBAC, access controls, and cost governance.
- Demonstrable experience designing and implementing modern data warehouses.
- Experience modeling data in cloud data warehouses such as Snowflake, Redshift, or BigQuery.
- Data Vault 2.0 experience strongly preferred; deep dimensional modeling experience acceptable.
- Demonstrated experience using testing frameworks to validate data and code in a production environment.
- Hands-on experience with data observability tooling.
- Proficiency using AI coding assistants (e.g., Claude Code, Cursor, Snowflake Cortex).
- Track record of leading technical projects end-to-end without a Product Manager.
- Comfort working in a regulated environment (e.g., SOX-relevant financial reporting).
Responsibilities
- Develop complex and efficient data pipelines to transform raw data sources into reliable, well-tested components of our data models.
- Design, build, and maintain data pipelines that deliver accurate, trusted data with the freshness our stakeholders depend on.
- Make data modeling decisions within our Data Vault 2.0 warehouse that balance raw fidelity in the vault with the consumption patterns of downstream marts and analytics.
- Administer and optimize Snowflake across warehouse sizing, query performance, access controls, RBAC and user/role management, and ongoing cost tuning.
- Build and maintain resilient Airflow DAGs and CI/CD pipelines that make deployments predictable, repeatable, and safe to roll back.
- Implement automated testing (unit, integration, and data quality) so issues are caught in CI before they reach production.
- Own production observability through our internal tooling, tuning alerts, responding to incidents, and closing the loop from production issues back into pre-load validation and CI checks.
- Leverage AI-powered tools as a core part of your development workflow to accelerate code generation, automate documentation, and improve code quality.
- Lead technical projects end-to-end, scoping with stakeholders, documenting requirements, and explaining technical trade-offs.
- Drive best practices for data governance, privacy, and security, including the change management and validation discipline required for SOX-relevant reporting.
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