Note: The job is a remote job and is open to candidates in USA. Pathstream is seeking an experienced Senior Analytics Engineer to join our Data team and drive the next chapter of how we use data to make decisions. This role involves partnering with business stakeholders to define metrics and conduct analyses, while also contributing to data modeling and engineering efforts within the team.
Responsibilities
- Partner with business stakeholders across Operations, Customer Success, Product, Growth, and Learning to scope analyses, define metrics, and answer the questions that drive decisions. Translate ambiguous, business-language requests into clear analytical work that holds up to scrutiny and leads to action
- Drive the analysis itself — bring judgment about what question is actually being asked, what the right analytical approach is, and what 'good' looks like. Push back when a request is underspecified and shape vague asks into rigorous analyses
- Own dashboards end-to-end with a strong business-intelligence mindset — design, build, maintain, and iterate based on stakeholder feedback to make data products that people actually use. You'll work in Hex (our primary BI tool), but we're looking for BI craft and judgment, not Hex-specific experience
- Adapt your message to the audience — surfacing the action-oriented takeaway for an executive, walking through the details with a peer, and producing written analyses that hold up after you've left the meeting
- Bring a data engineering and data architecture mindset — think about what data models should exist, how data flows between source systems and the warehouse, how schemas connect, and where pipelines need to be reinforced. Partner on data modeling decisions and contribute to the warehouse (in our case, primarily through dbt)
- Take ownership of data quality and trust. Build tests into the models you work on, set up monitoring on the dashboards and pipelines that matter most, and respond when something breaks. The bar is 'stakeholders should be able to trust what they see.'
- Collaborate closely with engineering — partner with the product and engineering teams on event instrumentation, source data quality, schema changes, and anything else upstream that affects what's available for analytics downstream. Be the analytics voice in engineering conversations, and the engineering-aware voice in analytics conversations
- Help shape the long-term direction of the data team — beyond day-to-day analysis, contribute your perspective on where the data architecture and analytics practice should be heading. Weigh in on what we should be measuring, how we should be instrumenting and collecting data, what tools belong in our stack, and where we should be investing for the next year and beyond. You won't own these decisions alone, but you'll be expected to bring informed opinions and help drive them forward
- Lean into an analytics culture where AI is already central to how we work. We use modern AI tooling (Claude Code, agentic skills) heavily in our day-to-day, and we're actively building out how the data team uses it
- Explore how AI-assisted workflows can improve the analytics craft - for SQL development, dbt modeling, dashboard prototyping, exploratory analysis, documentation, and code review. Bring practical experience and curiosity rather than buzzwords
- Contribute to how the data team develops reusable patterns, skills, and guardrails for working effectively alongside AI systems
- Help raise the bar across the small data team — review each other's SQL and dbt code, share patterns, and pair on tricky analyses. Be someone teammates want to learn from
- Bring others along in your work. Make decisions and trade-offs visible so the rest of the team learns from them, not just from the outcomes
- Operate as a steadying presence when priorities shift, requirements change, or analyses don't go the way anyone expected. Help the team stay focused on the decisions the work is meant to support
Skills
- 4–6 years of professional analytics experience with a track record of owning analyses and dashboards end-to-end that includes the messy front end: scoping ambiguous requests, asking the follow-up questions that reframe the work, and knowing when an analysis is actually answering the wrong question
- Experience partnering closely with non-technical stakeholders and knowing the full arc of good analytical work — from the discovery conversation that surfaces what's actually being asked, through the judgment calls mid-analysis, to findings presented in a way that drives action rather than more questions
- Strong SQL skills - comfortable writing complex queries, debugging joins, reasoning about performance, and working in a warehouse environment (Redshift, Snowflake, BigQuery, or similar)
- Comfortable reading and writing SQL inside a version-controlled codebase. Sees PR reviews as part of the craft, and sees modeling and transformation work as part of the analytics craft
- Working understanding of data modeling and schema design — can reason about facts vs. dimensions, grain, normalization trade-offs, and when to build a new model vs. reshape an existing one
- Genuine business-intelligence mindset, with hands-on experience in a modern BI tool (Hex, Looker, Tableau, Mode, Sigma, or comparable) and an opinion on what makes a dashboard good
- Thoughtful collaborator on AI-assisted analytics workflows. Curiosity and willingness to learn matters — and can speak from practical experience about what's worked and what hasn't in own work
- Hands-on experience with dbt or strong motivation to ramp quickly; working knowledge of Python is a plus
- Familiarity with version control workflows (git, PRs, code review) for analytics work, or eager to develop them
- Takes quality seriously — builds tests, checks own work, and understands that a trusted dashboard is worth more than a clever one
- Demonstrated cross-functional partnership — with business stakeholders (PMs, ops leads, growth) and technical counterparts (engineering, product)
- Strong written and verbal communication and can adapt message to the audience. Writes clearly enough that an executive can act on analysis without a follow-up meeting
- Comfortable with ambiguity, shifting priorities, and making pragmatic decisions to unblock the business
- Invests in the people around — through code review, mentorship, and the everyday work of helping a small team get better together
Benefits
- 100% employer-paid medical, dental, and vision insurance coverage for you and 50% for your partner/spouse and dependents
- Health, commuter, and parking flexible spending accounts
- Employee Assistance Program (mental health, financial health, legal support, and more)
- Free access to wellbeing apps like Ginger and Headspace
- Flexible paid time off and 13 paid holidays
- Generous paid parental leave
- Short and long-term disability insurance (100% company paid)
- Annual professional development budget
- Company-provided laptop
- Remote-first culture
- Life insurance (100% company paid)
- 401(k)
Company Overview