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We are seeking an experienced and driven Senior Analytics Engineering Lead to join our Core Analytics Team (CAT) within the Tech Ops & Analytics group. This is a unique opportunity to take on a leadership role, managing a team of data scientists and driving the technical vision for our analytics platform. You will play a crucial part in shaping how we leverage data to support key decisions across the organisation. This role bridges the gap between data engineering and data science, enabling you to have a significant impact on how we collect, analyse, and utilise data.

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

Core Analytics is responsible for “end-to-end” internal data & analytics across Google DeepMind, and sits within our Technical Operations & Analytics group. We aim to drive better organisational decisions – blending our technical skillset with rich stakeholder relationships to deliver impact. The outcome of our work ranges across topics like compute & experimentation, research organisation, and product analytics, and directly influence Google DeepMind’s progress towards our mission. You will join Core Analytics as a “Role Lead”, responsible for both people management and overall technical direction of our data science & data engineering efforts.

Key responsibilities include:

People Leadership

• Line manage data scientists on the team, fostering their growth and development through mentorship and performance management.
• Own the development plans and career progression opportunities for your team members.
• Collaborate with other CAT leads (especially our “Domain (tech) leads”) to understand their priorities and ensure the right team members are in place for project & individual success.
• Organise internal knowledge exchange and upskilling opportunities to ensure team members have the right data engineering & science skills needed to succeed and grow in their roles

Cross-Functional Tech Leadership

• Develop and implement a comprehensive technical vision and strategy for our data engineering, analytics platform development, and internal data warehousing efforts.
• Drive a coordinated approach to data ingestion, engineering, and analytics model design – balancing development velocity, production readiness (efficiency, latency, stability, scalability), self-service enablement, and data governance.
• Establish internal standards and processes to ensure efficient collaboration and codebase management across a dozen+ contributors.
• Drive our internal Data Governance strategy and implement appropriate tooling.
• Collaborate with Google partner teams to define requirements and ensure the successful implementation of features that support our needs.
• Identify opportunities to scale the use of the analytics stack beyond the immediate team and enable effective contributions from others within GDM.

Project Advisory & Delivery

• Serve as a “go to” advisor on data science topics across the organisation, providing your advice and expertise in service of developing a data-led culture
• Act as a senior individual contributor – organising and contributing directly to key strategic projects within your remit

In order to set you up for success in this role at Google DeepMind,  we are looking for the following skills and experience:

Proven expertise: A strong background in both data science and analytics engineering, with a demonstrated record of success in building and implementing data-driven solutions.Leadership experience: Experience leading and mentoring teams in a data-focused environment, with a focus on fostering growth and development.Strategic thinker: A proven ability to develop and implement data strategies that drive company-wide impact, including initiatives like self-service analytics, business intelligence system development, and development & deployment of data science products & solutions.Technical proficiency: Exceptional technical skills, including advanced knowledge of Python and SQL, a deep understanding of statistics and data modelling, and familiarity with modern data stack technologies.Collaborative approach: Experience collaborating effectively with software engineering teams to deliver data infrastructure solutions that meet the needs of the organisation.