Your Role:
We are looking for a Senior Analytics Engineer to lead the vision for the transformation layer of our data platform. This platform integrates data from transactional databases backing core Alpaca applications, API logs, CRMs, payment systems, and marketing platforms. We process hundreds of millions of events daily, a number that is rapidly growing as we onboard new customers.
We prioritize open-source solutions and use Google Cloud Platform (GCP) as the foundation of our data infrastructure. Our transformation layer, powered by dbt running through the Trino query engine, builds data models that are delivered to end users via BI tools, reports, and reverse ETL. Our stakeholders range from finance to operations, customer success, and the executive team, necessitating varying data availability (from monthly to near-real-time) and integrity (up to cent-level precision). You will be working alongside our Data Engineers who drive the ingress of the data and the infrastructure of the Lakehouse and related services, and Data Scientists who consume the data models and augment them with new features.
Our team is 100% distributed and remote.
Responsibilities:
- Develop scalable patterns in the transformation layer to support consistent BI tools integration across business verticals.
- Ensure data discoverability and maintain high standards of change management, including model testing and data monitoring, as Alpaca’s products evolve.
- Seamlessly integrate the Lakehouse with BI tooling to create repeatable ways of surfacing metrics to end users.
- Set a high standard for development practices, ensuring quality in new data models and their orchestration.
- Collaborate closely with finance, operations, customer success, and marketing teams to meet data modeling needs.
Must-Haves:
- 3+ years of experience in data engineering or data analytics, focusing on the transformation component of the ELT process.
- Experience building scalable transformation layers, preferably through formalized SQL models (e.g., dbt).
- Experience in Python for transformations beyond SQL.
- Experience with CI/CD and code version control.
- Strong hands-on experience with relational databases (e.g., Postgres, Iceberg), including query optimization.
- Ability to adapt quickly in a fast-paced environment and tailor solutions to evolving business needs.
- Experience with ETL technologies for ingestion (e.g., Airbyte) and orchestration (e.g., Airflow).
- Experience working in a cloud environment (AWS or GCP).
If you're passionate about analytics engineering and thrive in a dynamic startup environment, we'd love to hear from you!