Senior Analytics Engineer

About the role

This role focuses on the transformation layer within Alpaca's data infrastructure. The Senior Analytics Engineer will be responsible for designing, building, and maintaining scalable data models using dbt and SQL. These models will support a wide range of business needs, from monthly financial reports to near real-time operational metrics, requiring precision and reliability.

A key aspect of this position involves setting and enforcing technical standards for data modeling, development, testing, and monitoring. This includes ensuring data quality, integrity, and discoverability across the organization. The engineer will also create repeatable patterns for integrating data models with BI tools and reverse ETL processes to enable consistent metric reporting.

Success in this role means collaborating directly with various business teams, including finance, operations, customer success, and marketing, to understand their data requirements. The engineer will deliver reliable data products and champion high standards for development practices, such as robust change management, source control, code reviews, and data monitoring as the company's products and data evolve.

The salary for this role ranges from $117,000 to $127,000 USD annually.

Skills that matter here

• SQL: Expert-level SQL is required for complex queries and data transformations within the data models.
• DBT: This role involves using DBT for designing, building, and maintaining scalable data models.
• Python: Proficiency in Python is needed for transformations that extend beyond SQL capabilities.
• Postgres: Experience with query optimization across OLTP systems like Postgres is relevant for this position.
• Iceberg: Hands-on experience with query optimization across OLAP systems such as Iceberg is required.
• GCP: Familiarity with cloud environments like GCP is beneficial for this role.

Who this role suits

• A person who thrives on owning data products from start to finish, ensuring their quality and scalability.
• Someone who is comfortable navigating ambiguity and proactively identifies improvements in data warehouse performance.
• An individual who excels at collaborating with diverse stakeholders to define requirements and deliver reliable data solutions.
• A professional who can establish and enforce best practices for data modeling and development with minimal oversight.

From the employer

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).