Analytics Engineer - Product Operations

Product Operations is a fast-growing, global team of data-driven influencers, setting the standard for how data and analytics is used at HubSpot, and inspiring product teams to action. We draw from multiple sources of data (user event data, NPS, transactional data, etc) to discover what is going well, where we can improve, and what to build next, partnering with Product Management, User Experience, and Engineering leadership to build better solutions for our customers.

The Analytics Engineer serves as a force multiplier for the broader Prod Ops org: building and maintaining our highest value data assets; defining and implementing team strategy for analytics development processes; and enabling the team to operate with a consistent level of excellence as we scale in a remote, global environment. In this role, you will work cross functionally, developing a deep understanding of the business and analytics priorities for the Prod Ops org to motivate solutions that will unlock our ability to scale and drive actionable insights - from refining and extending our data models, to refactoring tables, to building new assets and training content. You will collaborate with distributed analysts across the company to drive alignment on key business logic and metric definitions, influence the evolution of our core analytics infrastructure, and contribute to HubSpot’s library of best practices.

In this role, you’ll get to:

Collaborate with technical and non-technical stakeholders, bridging the gap between business problem and technical solution
Design and collaborate on Product Operations’ “crown jewel” data assets that answer HubSpot’s most critical operational questions, reinforcing them as Source of Truth across the organization
Develop scalable data models that enable performant analysis into Hubspot’s products and business
Curate, organize, and document Product Operations data and reporting environments across Looker, dbt and Amplitude
Collaborate closely with Hubspot’s BI and Data Engineering teams to expand data access and availability
Codify and democratize best practices for SQL query optimization/reusability and reporting, leveraging HubSpot’s data assets, and analytics development within Snowflake SQL, dbt, LookML, and git
Influence and evolve teamwide coding standards, documentation requirements and development processes (code reviews, testing requirements, etc)
Scope requirements with internal stakeholders and lead working groups to usher projects through their entire lifecycle while building clear roadmaps for cross-functional team members
Learn from our team of Analytics engineers, as well as HubSpot’s greater AE community

We are looking for people with experience in the following areas:

Basic Qualifications:

Development

Experience with technical execution of high complexity business intelligence and analytics projects in code with the use of CTEs, nested queries, or other similar techniques
Experience with complex datasets and computer science fundamentals, including software development lifecycle (SDLC)
Experience building and optimizing data pipelines, architectures and data sets; experience diagramming architecture and entity relationships with Lucidcharts (for example)
Understanding of multi-step ETL and ELT jobs / data pipelines and working with job scheduling systems, with an ability to reverse engineer and refactor existing technical projects

Analytics

Strong understanding of the analyst’s workflow as it relates to both structured and unstructured datasets
Ability to scope and document technical designs
2+ years of hands on experience experience with advanced SQL (writing and optimizing), cloud data warehouses (eg Snowflake, Redshift, Bigquery) and relational databases
Ability to collaborate through code-management/version control tools like GitHub Enterprise (for peer-reviews, feature branches, resolving conflicts and commits)

Team Enablement

Experience training and onboarding colleagues (especially at-scale, or in a remote-first or global environment)
Effective communication across multiple modes (video, wikis, decks, guided exercises) and a knack for choosing the best format for the task and audience at hand
Writing and reviewing end-user and technical documents, including requirements and design documents for existing and future data systems, as well as data standards and policies

Preferred Qualifications:

Development

Exposure to data pipeline and workflow management tools (i.e. Airflow, Google Cloud Composer, Luigi, etc)
Experience with script based analytic transformation tools (dbt, AWS Glue, Talend, etc)
2+ years of experience with data quality and validation exercises in data science, business analytics, business intelligence (BI), or comparable big data “like” environments

Analytics

Experience with JSON Flattening and extracting internal elements
Conceptual knowledge of Looker (LookML, Looks and Dashboards) or equivalent BI visualization tool (e.g. Tableau, Qlik, Power Bi)

Team Enablement

Experience identifying and driving process improvements around data use to drive team effectiveness
Best practice definition: identifying the minimal set of rules for the greatest teamwide gains in clarity, accuracy, discoverability, and reusability
Patience, empathy, and finding success in the team’s success

Cash compensation range: 100,000 - 125,000 USD annually

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