Sr. Analytics Engineer

About this role

Senior Analytics Engineer

Pathstream | Data | Remote

ROLE/ DEPARTMENT OVERVIEW

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 is a senior individual contributor role reporting to the Director of Insights & Operations.

We are looking for someone who has already built a strong foundation in stakeholder-facing analytics and the engineering craft that sits underneath it, and who now brings the judgment, range, and ambition to operate end-to-end across the analytics stack — from ambiguous business questions through to the data models and pipelines that answer them.

A core expectation of this role is the ability to advance Pathstream's AI-native data practices. AI adoption is already deeply embedded in how we build software at Pathstream, and the data team is no exception. We are looking for someone who not only embraces this approach but actively pushes the data team forward in adopting AI-assisted analytics workflows.

WHAT WE DO

Pathstream was founded on the premise that substantial unrealized economic and human potential exists within each organization's frontline workforce. We are reshaping the way employees and managers on the frontlines excel in their careers. In this age of rapid tech evolution, our enterprise partnerships transform frontline teams from transactional to relational, and from task-oriented to strategic.

We impact companies' bottom lines with improved productivity, increased retention, and differentiated quality of customer experience — while employees experience greater success in their current roles and unlock future professional growth.

We're a tight-knit, hardworking, mission-driven team of ~60 employees eager to scale our solution and transform careers across the United States.

THE ROLE

ANALYSIS & STAKEHOLDER PARTNERSHIP

• 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.

DATA MODELING & ENGINEERING

• 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.

AI-ASSISTED ANALYTICS

• 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.

TEAM CRAFT & COLLABORATION

• 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.

ABOUT YOU

EXPERIENCE

• You bring 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.

• You've partnered closely with non-technical stakeholders and know the full arc

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