Senior Data Analyst, Product

Articulate

1d•Remote

About The Position

Articulate is looking for a Senior Product Data Analyst to join our amazing Data team! As a Senior Product Data Analyst, youâll collaborate closely with one of our product teams to collect and analyze data that helps us build the best experience for our customers. You'll use structured and unstructured data to understand customer problems and behavior through statistical analysis and data visualization. Youâll conduct ad-hoc analyses and build data tools, such as dashboards and reports, to enable ongoing data exploration and improve our data infrastructure.

Requirements

5+ years of experience in data analysis, business intelligence, or a related quantitative field.
Demonstrated experience in product analytics for a B2B SaaS product, including funnel analysis, retention modeling, cohort analysis, and feature adoption metrics.
Strong proficiency in SQL, including both ad-hoc querying and data modeling for analytical reporting, especially for event-level datasets and behavioral analytics.
Strong experience instrumenting and validating product usage event data, preferably using Segment or comparable customer data platforms.
Experience with at least one data visualization platform (Looker or Metabase preferred) with the ability to design intuitive dashboards and optimize reporting structures.
Ability to validate data across complex or undocumented systems, ensuring accuracy and consistency across outputs.
Proven ability to translate product questions into structured analytical approaches that inform feature development and product strategy.
Experience partnering directly with product managers, UX researchers, and engineers to define metrics, design experiments, and support product discovery.
Ability to communicate user behavior insights in a compelling narrative that informs product decisions for both technical and non-technical stakeholders.

Nice To Haves

Experience defining and maintaining a product analytics tracking plan or taxonomy.
Experience implementing best practices for client-side and server-side event tracking, including schema governance and QA workflows.
Experience using Mixpanel or a similar tool to visualize user behavior funnels and analyze product dataExperience working with product experiment platforms and A/B testing.
Familiarity with data engineering or analytics engineering concepts (e.g., dbt, ETL workflows, version control, data model documentation).
Experience with survey feedback analysis and qualitative analysis.

Responsibilities

Serve as a strategic analytics partner to product managers, UX researchers, and engineers to define analytics requirements for new product features, including identifying key user behaviors and success metrics.
Create scalable product analytics dashboards and self-service tools tailored to product managers, UX partners, engineers, and designers, using SQL, Looker, Mixpanel, Metabase, Python, or R.
Partner with engineering and analytics engineering to implement and QA product event collection with Segment, ensuring events follow naming conventions, are consistently structured, and are modeled for high-quality downstream analytics; audit event coverage to identify gaps and improve the reliability of behavioral data.
Conduct deep-dive behavioral analyses (e.g., funnel, cohort, time-to-value, retention, feature adoption) to uncover user needs, friction points, and opportunities to improve the product experience.
Synthesize quantitative data with qualitative insights to inform product strategy and shape feature roadmaps.
Build frameworks that measure long-term product engagement, user journeys, and the impact of new features on customer outcomes.
Collaborate with product teams to design, instrument, and analyze A/B tests and other experimental methodologies; provide interpretation of lift, impact, and risks.
Support product discovery by developing hypotheses, generating exploratory analyses, and identifying emergent behavioral patterns.
Validate data accuracy by comparing results across source systems and performing root-cause analysis on anomalies or unexpected metric behavior.
Enable self-service analytics by educating stakeholders on data sources, metric definitions, and reporting tools; identify opportunities to improve clarity and usability of reporting.
Partner with Analytics Engineering and Data Engineering teams to define data requirements, improve data quality, and ensure reliable data pipelines and modeling layers.
Contribute to and uphold team best practices for data modeling, visualization standards, and documentation.
Participate in peer review processes for data models, dashboards, and analyses, ensuring quality, consistency, and alignment with team standards.
Share knowledge and emerging best practices with teammates, contributing to documentation and helping strengthen data literacy across the organization.
Support hiring processes for new analysts by participating in interview loops or technical assessments, as needed.