Head of Product Analytics

Key Skills

Data Engineering, Data science techniques, Effective communication skills, Finance, Infrastructure as code, Looker, Machine learning techniques, Python Programming, SQL, Tableau

Job Description

This role will develop the analytics engineering programme and the capabilities across the analytics organisation. They will work to train, hire, and manage the performance of the team, tools, and standards put into place.

This is a unique opportunity for an established analytics engineering leader who enjoys the fast paced environment of a growing company, has vast experience in analytics engineering and has a passion to lead teams helping the Bumble Inc. mission to foster kind connections.

You would ideally have a background in analytics engineering at either a dating, social, gaming or other relevant tech company, with proven experience of driving data quality, sources of truth, KPI alignment, and optimisation of data warehouse consumption costs.

You understand the high-level business model of the company and its primary drivers to build strategies and teams that respond to ever changing business needs. You will help senior leadership across tech and the business navigate ambiguity and shape analytical roadmaps, projects and insights briefs.

You bring experience of working with large multi-disciplined analytics teams and complex data sources. You will be fluent in a range of analytical and engineering techniques from data modelling and governance, quality, reliability, and scalability, self-serve analytics, to onboarding and overhauling data stacks with new vendor solutions.

Internally, this role is known as Associate Director, Analytics Engineering.

KEY ACCOUNTABILITIES

• Transform the analytics organisation to onboard the analytics engineering capability, whilst leading, shaping, and hiring a team of analytics engineers.
• Onboard analytics engineering tools and rolling out with the relevant talent and standards across decentralised organisations.
• Set up best practices around data governance, quality, observability, and lineage.
• Run a hybrid model of analytics engineers embedded across a large multi-domain organisation while managing partnership with business stakeholders, data science, and data engineering.
• Create a data dictionary, business-wide metric and taxonomy alignment which reflects the data mart infrastructure.
• Enforce governance standards across non-technical analysts to manage consumption costs and efficiencies of queries.
• Lead teams to create enterprise level reporting solutions that align stakeholders and feed into external reporting procedures around a common source of truth.
• Run an RFP process and rollout of an enterprise visualisation solution.
• Own the business data model design and ownership distribution as well as best practices around tooling, maintenance, and incident management.
• Own the analytics engineering roadmap and be able to flex resource as required.
• Scale strategic initiatives which benefit all brands by creating fungible resourcing distributed by skill and capability. This means creating a centralised view of initiatives across brands, understanding the level of effort and skillsets required, and managing the team as needed.
• Set and track OKRs for the data programme at large, with a focus on quality, reliability, speed, and accessibility.

REQUIRED EXPERIENCE & SKILLS

• Preference for a graduate degree in Mathematics, Engineering, Information Sciences, Economics, Finance, or STEM. PhD and Masters welcome.
• Preference for experience working in similar dating/social/gaming tech product industries or else financial services/high-data-volume industries.
• You will have had ample experience working with a large distributed team of analysts and analytics engineers and across multiple brands and levels.
• You will have rolled out an analytics engineering programme in a large enterprise before, showing success and impact to the data quality and ROI.
• You will have on-boarded data tooling across the entire data value chain and are familiar with different combinations of vendors, their optimal ways of integrating with each other, and are able to advise on the commercial agreements, RFPs, and onboarding processes across timelines, domains, and brands. You have established partnerships and relationships with the most relevant vendors.
• You will have led an enterprise-wide data clean-up before, re-establishing reporting, sources of truth, KPI alignment, and a dashboarding ecosystem relevant for all stakeholders.
• You have ample experience architecting a distributed analytics engineering programme, knowing how to manage competency frameworks, training, and distributed ownership of a decentralised data mart network.
• You have experience working with complex data infrastructures and have experience partnering and guiding the work of data engineering to help facilitate ingestion, warehousing, and optimisation of databases. You have made established partnerships with engineering to help drive data collection quality.
• 9+ years experience in leading analytics engineering, data engineering, or automation teams, working in a fast paced environment.

ABOUT YOU

• Strong believer in Bumble Inc.’s brand vision and values
• Engaging, inspiring and clear communicator. A proven track record of overhauling data landscapes and engaging stakeholders to get their buy-in.
• Strong automation and growth mindset that is cascaded to the teams.
• Deep engineering and data infrastructure understanding of both 3rd party vendors and bespoke in-house solutions.
• Experience in guiding the planning and prioritisation efforts of your teams in a busy and fast moving environment.
• Experience working with legal, infosec, and 3rd party vendors, understanding how to maximise these relationships to drive value for the team.
• Experience with python/SQL, visualisation tooling such as Looker/Tableau, and data stack tooling across transformation, collection, and data science.
• Strong experience with driving ROI of an analytics programme.
• Strong governance and data quality mindset.
• Understands and can demonstrate machine learning and advanced analytical product impact in an organisation.