Analytics Engineer

Salary Range: 120000 to 150000 (Currency: USD) (Pay period: per-year-salary)

We want to hear from people who…

• Have successfully designed and implemented a fit-for-purpose data model optimized for action-oriented analytics use cases and company-wide reporting.
• Have experience creating and maintaining scalable, cost-effective data pipelines to manage and transform large amounts of data and ensure a high-quality data model.
• Can partner with Product and Engineering to integrate data upstream and downstream across our customer engagement platforms (Hubspot, Stripe, etc.).
• Know how to establish and enforce robust data integrity standards to ensure data is consistent, timely and accurate.

Responsibilities

• DATA MODELING
• Gain a deep understanding of data needs and use cases, partnering with stakeholders and analysts to understand what’s needed in the short- and medium-term.
• Own the creation and execution of the data model that supports these needs, with a focus on quality, usability, and adaptability in a fast-changing environment.


• PIPELINE BUILDING AND MANAGEMENT
• Own the creation and execution of data pipelines that drive business intelligence and analytics across the company.
• Partner with Product and Engineering in ingesting and transforming data into a reliable, usable, and scalable structure.
• Own the company’s data stack (we use Snowflake, dbt, Fivetran, Mozart, and Hex).
• Consistently deploy solutions that improve the efficiency of data transforms, with a focus on speed, cost effectiveness, and stability.


• DATA INTEGRATION
• Define what needs to be tracked in our different platforms and translate these into technical requirements for Product and Engineering.
• Drive the future roadmap of Product and Revenue teams from a data systems perspective.


• DATA GOVERNANCE AND TESTING
• Implement and ensure consistency in data, metrics definition, and terminology across all data products: data warehouse, KPIs, dashboards, reports, and self-serve.

Requirements

• Advanced degree in Statistics, Computer Science, Mathematics or a related field, or relevant industry experience.
• Expert programming and data manipulation skills using SQL and Python (or any coding language).
• Ability to proactively drive initiatives with minimal oversight. You understand when needs arise and creatively find solutions.
• Experience developing data models and designing data warehouses.
• Experience building multi-step ETL jobs/data pipelines.