Design, implement, and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources.
Build and optimize warehouses and marts (e.g., Snowflake, BigQuery) for analytics, reporting, and product use cases.
Ingest data from APIs and SaaS platforms (e.g., CRM, financial data APIs) into the core data platform.
Design conceptual, logical, and physical models to deliver scalable, consistent, high‑quality datasets.
Implement validation, schema management, and documentation to ensure accuracy and compliance.
Monitor and tune pipeline/warehouse performance for scalability and cost efficiency.
Apply data security and privacy controls aligned with financial regulations; ensure full transformation traceability.
Deliver clean, consistent datasets to analysts, PMs, and operations for fast, data‑driven decisions.

Bachelor’s/Master’s in CS, Engineering, or related field.
3+ years in data engineering or similar backend data‑focused role.
Strong SQL and Python for transformation and automation.
Experience with modern ETL/ELT frameworks (e.g., dbt).
Proficiency with cloud platforms (AWS preferred) and serverless data services.
Strong experience with data warehouses (Snowflake preferred).
Skilled in API integrations and ingestion from third‑party systems.
Proficient in data modeling (Kimball/Star, Data Vault).
Able to implement CI/CD for data workflows and set up logging/monitoring/alerting for jobs.

Competitive salary; high ownership from day one.
Fast decision cycles; remote‑first with flexibility on hours/location.
Direct access to founders; clear expectations, regular feedback, and growth support.
Work on complex, high‑impact problems in a data‑intensive industry.

Data Engineer

Key skills