Analytics Engineering Manager

The Analytics Engineering team is part of the Data Engineering organization, whose mission is to build reliable and trusted data sources and tools that enable timely and accurate data-driven decision-making. Sitting at the intersection of data engineering, data science, and business analytics, our team transforms raw data into actionable insights through solid pipelines, scalable data models, and intuitive tools. By prioritizing data quality, reliability, and usability, we make sure stakeholders can trust and leverage data to drive real business impact.

What you’ll be doing:

This role combines technical expertise with business impact, whether it’s building robust data pipelines or directly solving business problems and delivering insights.

As the leader of the analytics engineering team, you’re going to:

• Build and lead the team: Hire, mentor, and grow a team of analytics engineers who will work closely with product and data science teams.
• Develop deep product expertise: Ensure your team deeply understands product data, building targeted data marts and tools that solve real business problems.
• Leverage AI and LLMs: Investigate how LLMs and AI can change analytics and build data foundations that support these future needs. Focus on creating data marts optimized for LLMs and AI-driven analytics.
• Unlock the value of our data: Partner with stakeholders to maximize the commercial impact of data by building scalable models, optimizing pipelines, and integrating cross-product data for better decision-making.
• Directly deliver business impact: Oversee the creation of dashboards, ad-hoc analytics, and self-service tools that empower product teams to make data-driven decisions.
• Prioritize outcomes over tools: Leverage the right frameworks and technologies to drive value, whether by developing abstractions, creating internal data apps, or improving scalable workflows.
• Bridge the gap between business and data: Liaise between product data science, product managers, and central data engineering. Ensure your team uses central data tools and infrastructure while remaining agile and product-focused.

What We Look For in You

We’re looking for a hands-on leader to build and manage the Analytics Engineering team, bridging data engineering and product data science. Your team will create domain-specific data marts, tools, and analytics solutions that drive our products and business. You’ll build tailored data solutions while staying aligned with data engineering best practices. This is a chance to shape the future of analytics as we integrate LLMs and AI-driven insights.

Success in this role requires a mix of leadership, technical expertise, and product mindset. Here’s what we’re looking for:

• Resourceful problem solver: You thrive on tackling new and complex challenges, even those outside of your expertise. Whether it’s learning a new programming language, diving into an unfamiliar dataset, or seeking insights from domain experts, you do whatever it takes to find the best solution. Difficult or ambiguous problems don’t frustrate you; they motivate you.
• AI-forward: You’re excited about the role of LLMs and AI in analytics, leveraging them to boost productivity while applying prompt engineering and design to improve response accuracy and relevance. You bring forward-thinking ideas on how to prepare for an AI-driven future.
• Experienced in building and leading teams: You have experience hiring and managing data teams, and know how to inspire and grow talent.
• Hands-on tech lead: You’re comfortable balancing hands-on work with strategic leadership.
• Data modeling and tools: You’re an expert in data modeling, ETL/ELT, and modern data stack tools (e.g., Airflow, DBT, Snowflake, Hex).
• Engineering best practices: You’re comfortable with version control (GitHub), CI/CD, modern development workflows, OOP, building scalable frameworks, and advanced SQL for data transformation, querying, and optimization.
• Creative and detail-oriented: You bring out-of-the-box thinking, meticulous attention to detail, and a sense of urgency to every project.
• Autonomous and accountable: You operate with a high degree of independence while taking full ownership of outcomes.
• Product and business sense: You’ve collaborated with product and data science teams to deliver analytics solutions, you can quickly understand product goals, prioritize tasks, and address business challenges through analytics engineering.
• Clear and influential communicator: You communicate clearly and know how to get buy-in for your team’s work, build relationships across teams, and break down silos.
• Strong statistical foundation: You have a strong understanding of statistics and probability, enabling you to interpret data effectively, validate assumptions, and support data-driven decision-making.

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