Baselayer is revolutionizing the way businesses approach fraud prevention and compliance by building rock-solid infrastructure that powers cutting-edge AI/ML products for financial institutions.
Requirements
- 1–3 years of experience in data engineering, working with Python, SQL, and cloud-native data platforms
- Built and maintained ETL/ELT pipelines, and you know what clean, scalable data architecture looks like
- Comfortable with structured and unstructured data, and you thrive on building systems that transform chaos into clarity
- Think in DAGs, love automating things with Airflow or dbt, and sweat the details when it comes to data integrity and reliability
- Curious about AI/ML infrastructure, and you want to be close to the action—feeding the models, not just cleaning up after them
- Value ethical data practices, especially when dealing with sensitive information in environments like KYC/KYB or financial services
Responsibilities
- Design, build, and maintain robust, scalable ETL/ELT pipelines that power analytics and ML use cases
- Own the architecture and tooling for storing, processing, and querying large-scale datasets using cloud-based solutions (e.g., Snowflake, BigQuery, Redshift)
- Implement rigorous data quality checks, observability tooling, and alerting systems to ensure data integrity across environments
- Create efficient, reusable data models using tools like dbt, enabling self-service analytics and faster experimentation
- Partner with security and compliance teams to ensure data pipelines adhere to regulatory standards (e.g., SOC 2, GDPR, KYC/KYB)
- Continuously optimize query performance and cost in cloud data warehouses
- Stay on the cutting edge of data engineering tools, workflows, and best practices—bringing back what works and leveling up the team
Other
- You want to learn from the best of the best, get your hands dirty, and put in the work to hit your full potential.
- You’re a translator between technical and non-technical stakeholders, aligning infrastructure with business outcomes
- Highly feedback-oriented. We believe in radical candor and using feedback to get to the next level
- Proactive, ownership-driven, and unafraid of complexity—especially when there’s no playbook
- Hybrid in SF. In office 3 days/week
