Company

SaaS

ProductDataAnalyst

€75–110k ~AI est. Ireland FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Product Data Analyst. Skills: Product analytics, User behavior analysis, A/B testing, Data-driven recommendations. Act as analytics partner to product managers. Support analysis of product usage”

What You'll Achieve.

Influence product roadmap decisions; Provide data-backed recommendations on what to build; Provide data-backed recommendations on what to improve; Provide data-backed recommendations on what to deprioritize

Industry & Context.

SaaS
Problems you'll solve

Analytical approaches; Identify analytical opportunities

What They're Looking For.

Must Have

5+ years of experience in product analytics, Experience analyzing user behavior, Deep expertise in SQL, Understanding of statistical methods, Proven experience designing, running, and interpreting A tests, Experience defining event tracking schemas, Ability to independently identify high-impact analytical opportunities, Excellent communication skills, Product intuition

Nice to Have

Python experience for data analysis is a plus, Familiarity with causal inference methods is an advantage, Familiarity with experimentation platforms is an advantage, Experience working with technical or developer-focused products is a plus

What You'll Do.

Act as analytics partner to product managers

Support analysis of product usage

Support analysis of user behavior

Support analysis of feature performance

Conduct advanced product analyses

Identify key behavioral drivers

Deliver cross-product insights

Deliver segment-level insights

Co-develop hypotheses with product managers

Design analytical approaches

Influence product roadmap decisions

Provide data-backed recommendations

Ensure statistical rigor

Ensure actionable outcomes

Partner with engineering on event instrumentation

Partner with product on event instrumentation

Partner with engineering on tracking design

Partner with product on tracking design

Partner with engineering on data quality improvements

Partner with product on data quality improvements

Identify opportunities for deeper analysis proactively

Surface recommendations

Translate complex data findings

Support decision-making

Contribute to improving analytical standards

Contribute to improving methodologies

Contribute to improving experimentation practices

How You'll Work.

Team & Collaboration

Product managers; Engineering teams; Product teams

Communication Scope

Clear narratives; Decision-ready insights

Full Job Description

## Accountabilities Act as the dedicated analytics partner to product managers, supporting deep analysis of product usage, user behavior, and feature performance. Conduct advanced product analyses across activation, onboarding, adoption, retention, and expansion to identify key behavioral drivers. Deliver cross-product and segment-level insights that provide a broader perspective beyond individual product squads. Co-develop hypotheses with product managers and design analytical approaches to validate product decisions. Influence product roadmap decisions by providing clear, data-backed recommendations on what to build, improve, or deprioritize. Design, evaluate, and interpret A/B tests and experiments, ensuring statistical rigor and actionable outcomes. Partner with engineering and product teams on event instrumentation, tracking design, and data quality improvements. Identify opportunities for deeper analysis proactively, surfacing insights and recommendations even without formal requests. Translate complex data findings into clear, structured narratives that support decision-making at all levels. Contribute to improving analytical standards, methodologies, and experimentation practices across the product organization. Requirements 5+ years of experience in product analytics, data analysis, or related roles within B2B SaaS environments, ideally in product-led growth companies. Strong experience analyzing user behavior, product funnels, activation metrics, retention drivers, and feature adoption. Deep expertise in SQL and strong familiarity with modern data stacks (e.g., BigQuery, dbt, Metabase or equivalent tools). Strong understanding of statistical methods, including hypothesis testing, bias detection, and experimental design. Proven experience designing, running, and interpreting A/B tests and product experiments. Experience defining event tracking schemas and collaborating with engineering teams on instrumentation. Strong ability to independently identify high-

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