# Senior Analytics Engineer

**Company:** [Alpaca](https://hotfix.jobs/companies/alpaca)
**Location:** Remote
**Role:** Data Engineering
**Experience:** 4+ years
**Skills:** SQL, dbt, Python, Postgres, Iceberg, Cube, Dbt Semantic Layer, Git, GCP, AWS
**Posted:** 2026-04-24

> Designs, builds, and maintains scalable data models using dbt and SQL for business reporting and metrics. Collaborates with stakeholders, ensures data quality, and optimizes ETL processes. Requires 4+ years experience with expert SQL/dbt/Python skills.

## Job Description

## What You'll Do

- Own the Transformation Layer: Design, build, and maintain scalable data models using dbt and SQL to support diverse business needs, from monthly financial reporting to near-real-time operational metrics.
- Set Technical Standards: Establish and enforce best practices for data modelling, development, testing, and monitoring to ensure data quality, integrity (up to cent-level precision), and discoverability.
- Enable Stakeholders: Collaborate directly with finance, operations, customer success, and marketing teams to understand their requirements and deliver reliable data products.
- Integrate and Deliver: Create repeatable patterns for integrating our data models with BI tools and reverse ETL processes, enabling consistent metric reporting across the business.
- Ensure Quality: Champion high standards for development, including robust change management, source control, code reviews, and data monitoring as our products and data evolve.

## What You Need (Must-Haves)

- 4+ years of experience in analytics engineering or data engineering with a strong focus on the \"T\" (transformation) in ELT.
- Proven track record of owning data products end-to-end, applying analytics and data engineering best practices to ensure data quality, scalability, and robust data models.
- Comfortable working with ambiguity and collaborating with stakeholders to define requirements; able to take ownership with minimal oversight in a fast-paced environment.
- Experience proactively identifying and implementing improvements to data warehouse performance and ETL efficiency.

### Technical Versatility

- Expert-level SQL and DBT skills for complex queries and data transformations.
- Proficiency in Python for transformations that extend beyond SQL.
- Hands-on experience with query optimization across OLTP and OLAP systems (e.g., Postgres, Iceberg).
- Proficiency with Semantic Layer modelling (e.g. Cube, dbt Semantic Layer).
- Experience owning CI/CD workflows and establishing team-wide standards for version control and code review (e.g., Git).
- Familiarity with cloud environments (GCP or AWS).

## Nice to Haves

- Experience with data ingestion tools (e.g., Airbyte) and orchestration tools (e.g., Airflow).
- Domain experience for brokerage operations or a passion for financial markets and modelling financial datasets.

## How We Take Care of You

- Competitive Salary & Stock Options
- Health Benefits
- New Hire Home-Office Setup: One-time USD $500
- Monthly Stipend: USD $150 per month via a Brex Card

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