Senior Analytics Engineer

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Pathstream

WebsiteLinkedInAll Job Openings

51 - 200 employees

📚 Education

👥 HR Tech

⚡ Productivity

💰 Venture Round on 2021-08

Education • HR Tech • Productivity

Pathstream is a company focused on upskilling and empowering workers with critical digital skills needed for the future of work. They offer programs that enhance productivity by providing targeted skill-building and career clarity. Their solutions aim to improve frontline workforce retention and drive digital transformation in businesses by developing a more tech-savvy workforce. Pathstream also champions diversity by helping to build a diverse talent pipeline. They combine AI-driven skill-building with human coaching to support career development and enhance managerial capabilities, ensuring employees are well-equipped for evolving roles in the digital world.

📋 Description

• 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.

🎯 Requirements

• 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 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 • You have strong SQL - comfortable writing complex queries, debugging joins, reasoning about performance, and working in a warehouse environment (Redshift, Snowflake, BigQuery, or similar). • You are comfortable reading and writing SQL inside a version-controlled codebase. You see PR reviews as part of the craft, and you see modeling and transformation work as part of the analytics craft. • You have a working understanding of data modeling and schema design — you can reason about facts vs. dimensions, grain, normalization trade-offs, and when to build a new model vs. reshape an existing one. You don't need to have led a warehouse build, but you should have opinions about how data should be structured. • You bring a 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. • You are a thoughtful collaborator on AI-assisted analytics workflows. You don't need to be an AI expert, but curiosity and willingness to learn matters — and you can speak from practical experience about what's worked and what hasn't in your own work. • You have hands-on experience with dbt or strong motivation to ramp quickly; working knowledge of Python is a plus. • You bring familiarity with version control workflows (git, PRs, code review) for analytics work, or are eager to develop them. • You take quality seriously — you build tests, you check your own work, and you understand that a trusted dashboard is worth more than a clever one.

🏖️ 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)