Remote Data Insights & Investigation Specialist

Who we are:

Marfeel is a fast-growing SaaS company transforming the way digital publishers understand and grow their audiences. Our real-time analytics and activation platform empowers newsrooms and content teams to make data-driven decisions that drive engagement, loyalty, and revenue.

Headquartered in Barcelona with a globally distributed team representing over 15 nationalities, we’re proud of our diverse and collaborative culture. Our people are the driving force behind our innovation—and we’re just getting started.

About the role:

We’re hiring a Data Insights & Investigation Specialist who blends sharp investigation with clear storytelling. You’ll partner with leading publishers such as Business Insider, El País, McClatchy and Webedia to make sense of the 100+ billion data rows we ingest every day.

You’ll bring clarity to our data by analysing how our metrics relate to external sources, explaining any relevant differences (methodology, taxonomy, tracking, attribution, timing), and coordinating with stakeholders on actions and follow-up checks.

In parallel, you’ll turn trusted data into narratives: mining large, multi-source datasets to surface trends and angles, then crafting publishable reports, benchmarks, and deep dives that Marketing, Sales, and Customer Success can use externally.

What you will do:

a) Internal investigations & data analysis (50%)

• Own internal investigations:
  Analyse client and internal platform data to identify anomalies or questions (including discrepancies vs. third-party sources such as GA4, GSC, social/video platforms and partner datasets), debug tracking/configuration issues, quantify gaps, determine root causes, and produce clear reports with recommended and/or implemented fixes.

• Work with large datasets:
  Write and optimise SQL, use light scripting for data preparation, build reproducible analyses, document queries/methodology, and ensure accuracy and performance.

• Use AI/LLMs to speed insight:
  Apply LLMs for data summarisation, outlier triage, and draft narratives; design prompts and guardrails; validate outputs against ground truth.

• Define methods & governance:
  Create standard operating procedures (SOPs/playbooks) for recurring checks (metric definitions, timezone alignment, UTM hygiene, bot filtering, deduplication).

• Partner with Product & Engineering:
  Raise well-scoped tickets for instrumentation changes, validate fixes with before/after analysis, and proactively surface opportunities to improve data quality and product experience.

b) Industry-facing studies & reporting (50%)

• Build insight narratives for the market:
  Transform quantitative findings and contextual research into publishable reports, benchmarks and studies for the industry, as well as concise internal summaries that help Product, Marketing, Sales and Customer Success.

This is you:

• Experience:
  
  4+ years in data/insights roles (SaaS, media, analytics, consulting, or similar high-performance environments) with examples of root-causing data mismatches and publishing actionable insights.

• Analytical toolkit: strong SQL skills (joins, CTEs, window functions); advanced Excel/Google Sheets; comfort handling very large datasets, and solid experience with relevant BI/dashboard tools.

• Modern analysis mindset: comfortable using LLMs (Claude, GPT/Codex) to accelerate exploration and drafting, paired with basic scripting (e.g., Python) and a rigorous validation against source data.

• Storytelling & communication: outstanding communicator and clear thinker who can translate complex analysis into simple, actionable recommendations and marketing-ready narratives, tailoring messages to different audiences; excellent written and verbal English.

• Project management: organized, deadline-driven, and proactive—can scope work, prioritize, and keep stakeholders aligned.

• Team player: able to collaborate across functions and levels, building strong relationships with internal stakeholders and external partners/clients.

• Quality mindset: detail-oriented with strong data hygiene; documents methods so work is reproducible and trustworthy.

• Education:
  
  Degree in a quantitative, business, or related field (or equivalent practical experience).