Senior Analytics Engineer

We are looking for a Senior Analytics Engineer who will supercharge data analysts, data scientists, and data-savvy stakeholders to more efficiently and effectively interact with our data stack. You will play a pivotal role in developing our data infrastructure processes and best practices across a wide range of areas including, foundational data models across multiple domains (growth, product, first-party data, finance, operations, etc.), self-service data tooling (e.g. feature stores, querying and data prep tools), data-powered apps, data governance and testing processes, documentation, and more. You take pride in developing tools, processes, and working frameworks that unlock your colleagues to better leverage data to generate insights, build models, operationalize data, and more. You thrive in a startup setting, where every day is different and the problems that you’re tackling often don’t have established solutions. You have a bias for action and are able to work in nuanced contexts where it is crucial to develop solutions that balance speed with scalability.

What You’ll Do:

Architect and build lasting data modeling solutions across a variety of data domains (growth, product, first-party data, finance, operations, etc.)
Communicate and collaborate with different stakeholders to identify opportunities and build solutions that standardize our data approach to common problems across the company
Monitor the health of our data pipelines and proactively discover (and solve) pain points and areas of fragility
Play a critical role in attacking opaque, unstructured problems by developing core analytic frameworks that shape the way the company thinks about our business
Increase data literacy and expertise through hands-on training and documentation
Establish and teach data governance, engineering, and testing best practices

What You’ll Bring to the Team:

4+ years of full-time working experience in quantitative analysis roles, with a significant portion of that time focused on data modeling in DBT and developing data pipelines and processes
Expert-level SQL skills
Strong knowledge of Python and associated analytics libraries (Pandas, etc.)
Strong experience using BI tools (ours is Looker) to effectively convey information
Strong problem-solving skills, analytical aptitude, and numerical dexterity
Experience with orchestration workflows like Airflow/Luigi
Familiarity with Unix/the command line
Demonstrated track record of project ownership

Nice-to-haves

Experience with CDPs like RudderStack or Segment
Experience with product event tracking design and implementation processes — partnering with product and engineering personnel
Experience managing ML model explainability and interpretability, as well as other data governance and testing processes
Experience with self-service data tooling - both third-party (ex. Tecton, SageMaker, Feast) and custom
Experience as a backend engineer or MLOps engineer

The target base salary for this position ranges from $116,450 to $152,500

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