Insights Manager (Remote)

We’re hiring an Insights Manager to lead a small team and deliver high-impact, decision-ready analytics across the business. You’ll sit in the central Analytics function, partnering cross-functionally with Data Engineering, BI and Product to turn data into reusable, production-quality insight products—not just one-off dashboards.

You’ll define analytical models (e.g., LTV, attribution, consumer insight and performance), manage and QA their build with Engineering/Analytics Engineering, and elevate stakeholder decision-making through sharp storytelling and clear recommendations. This is a hands-on leadership role with direct line management and real influence on what we build and why.

Where You’ll Drive Impact

Insight Products & Modelling

· Define and own analytical frameworks for retention, attribution, and funnel performance; manage the build and rollout with DE/AE.

· Translate business questions into well-scoped data problems and engineering and data science requirements; review logic and ensure documentation is clear and usable

· Shape experiment design (A/B, holdouts), success metrics, and readouts that drive product and media decisions.

Analytics Delivery & Storytelling

· Lead a team of analysts to deliver proactive, decision-first analysis (not just reporting).

· Produce campaign and business narratives that explain what happened, why it happened, and what to do next.

· Partner with BI to evolve Tableau assets into scalable, self-serve intelligence; rationalise overlapping views.

Collaboration & Engineering Partnership

· Work closely with Data Engineering/Analytics Engineering to turn insight logic into robust dbt models with testing, versioning, and CI.

· Brief and review data requirements for new pipelines and sources (GA4, Google Ads, Meta, affiliate/partner data).

· Contribute to AI/GPT-assisted insight (e.g., automated narratives, anomaly triage, assisted readouts) in collaboration with BI Ops and Product.

Team Leadership

· Line-manage 2–3 direct reports (with potential dotted-line mentorship of juniors): set goals, coach, review, and uplevel standards.

· Establish a repeatable delivery rhythm (intake → prioritisation → delivery → readout → productisation).

· Raise the bar on analytical quality, documentation, and reuse across the team.

Stakeholder Management & Influence

· Own the stakeholder map and cadence (SEM, Product, RevOps, GMs): set expectations, align on goals, and keep a clear delivery roadmap.

· Create summaries for senior stakeholders and present findings that translate analysis into decisions; document actions, owners, and timelines.

· Triage and prioritise inbound requests against team capacity; say “no” (or “not now”) constructively, with alternatives.

· Surface risks early, align trade-offs, and drive resolution with your team, Data Engineering/BI and business partners.

What You’ll Bring

· Technical toolkit: Strong SQL and dbt (data modelling, tests, documentation); Python for analysis/modelling (pandas, scikit-learn, NumPy); comfort with Git-based workflows.

· BI experience: Strong with Tableau (or similar: Looker, Power BI) including performance optimisation and stakeholder-ready design.

· Cloud experience: Experience working in GCP or similar cloud environments to deliver high quality data at scale

· Marketing & product fluency: Working knowledge of GA4, Google Ads, Meta, and growth/monetisation levers.

· Modelling background: Practical experience with attribution, segmentation, conversion prediction, and experiment analysis.

· Storytelling & influence: Ability to frame insights as clear recommendations tied to business impact; excellent written and verbal communication.

· People leadership: Experience coaching analysts, reviewing work, and running a predictable analysis delivery process.

· Experience level: Significant experience in analytics/insights roles in data-rich environments (typically 7–10 years). If you’ve built equivalent capability faster, we still want to hear from you.

Nice to Have

· Experience in affiliate/lead-gen or performance marketing environments.

· Exposure to LLMs/GPT for narrative generation, classification, or assisted analysis.

· Statistical depth (causal inference basics, uplift modelling, Bayesian thinking).