Senior Manager, Analytics Engineering

Coinbase Careers Page

2h•Hybrid

About The Position

Ready to be pushed beyond what you think you’re capable of? At Coinbase, our mission is to increase economic freedom in the world. It’s a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform — and with it, the future global financial system. To achieve our mission, we’re seeking a very specific candidate. We want someone who is passionate about our mission and who believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark on the world, who relishes the pressure and privilege of working with high caliber colleagues, and who actively seeks feedback to keep leveling up. We want someone who will run towards, not away from, solving the company’s hardest problems. Our work culture is intense and isn’t for everyone. But if you want to build the future alongside others who excel in their disciplines and expect the same from you, there’s no better place to be. While many roles at Coinbase are remote-first, we are not remote-only. In-person participation is required throughout the year. Team and company-wide offsites are held multiple times annually to foster collaboration, connection, and alignment. Attendance is expected and fully supported. 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. This role combines technical expertise with business impact, whether it’s building robust data pipelines or directly solving business problems and delivering insights.

Requirements

Experienced in building and leading teams: You have experience hiring and managing data teams (including managers), and know how to inspire and grow talent.
Relentless 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.
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.
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.
Experience in CX and Compliance analytics: You've partnered with customer support and compliance teams to build data solutions that improve operations, ensure regulatory accuracy, and enhance the customer experience.

Responsibilities

Build and lead the team: Hire, mentor, and grow a team of analytics engineers who will work closely with finance, CX, HR, compliance and data science teams.
Architect and lead core data modeling initiatives: Architect our core data models. Provide technical leadership while guiding a team of expert modelers, ensuring scalable, reliable, and high-impact data foundations.
Obsess over data quality and reliability: Create and enforce rigorous testing, monitoring, and validation frameworks to ensure data is accurate, consistent, and trusted at all times.
Develop deep domain expertise: Ensure your team deeply understands finance, CX, HR, compliance and 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 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.

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