Senior Analytics Engineer

📋 Description

• • As a Senior Analytics Engineer at Coinbase, you will play a pivotal role in shaping the future of economic freedom by building robust, scalable, and impactful data solutions. You will be instrumental in bridging the gap between raw data and actionable insights, empowering stakeholders across the organization to make data-driven decisions.
• • Your primary responsibility will be to transform complex data into reliable and understandable formats, serving as a foundational element for analytics and business intelligence. This involves developing and maintaining foundational data models that act as the single source of truth for various analytical needs throughout the company.
• • You will translate intricate business requirements into well-designed, scalable data models, dashboards, and tools, ensuring that our stakeholders have the resources they need to drive meaningful outcomes.
• • A key aspect of this role is fostering strong cross-functional collaboration. You will partner closely with engineering, data science, product, and business teams to ensure alignment on priorities, data strategies, and the successful implementation of data solutions.
• • You will be tasked with building and optimizing scalable data products, including frameworks, tools, and workflows that enhance efficiency for data users while upholding the highest standards of data quality and performance.
• • The role emphasizes an outcome-focused approach, leveraging modern development and analytics tools to deliver value rapidly. Simultaneously, you will ensure that these solutions are designed for long-term maintainability and scalability.
• • You will be expected to quickly develop deep subject matter expertise in specific business areas and data domains, understanding data flows from creation and ingestion through transformation and delivery.
• • This includes stepping into new lines of business, working with Engineering and Product partners to establish initial data pipelines and deliver critical insights.
• • You will proactively communicate with engineering teams to address and resolve data gaps that impact downstream data users, taking initiative and accountability for fixing issues across the entire data stack.
• • A significant part of your contribution will be generating tangible business value by interfacing with stakeholders on data and product teams. You will deliver commercial value from data, both directly and indirectly.
• • This might involve building new data models that enable multiple downstream Data Science teams to more easily unlock business value through experimentation and ad hoc analysis.
• • You will combine engineering details of algorithmic engines with statistical and data expertise to propose feasible solutions for improving these algorithms.
• • Collaborating with Product Managers, you will integrate new cross-product data into holistic frameworks to optimize key financing product business metrics.
• • The role champions a focus on outcomes rather than being solely tool-dependent. You will utilize a variety of frameworks and paradigms to identify the most effective tools for delivering value.
• • This includes developing new abstractions, such as User-Defined Functions (UDFs), Python packages, or dashboards, to support scalable data workflows and infrastructure.
• • You will have the opportunity to stand up frameworks for building data applications internally, empowering other Data Science teams to add value quickly.
• • When speed is paramount, you will leverage established tools like SQL and Google Sheets with mastery to deliver immediate impact.
• • You will be expected to think outside the box, demonstrate meticulous attention to detail, possess a strong sense of urgency, and operate with a high degree of autonomy and accountability.
• • Your expertise in data modeling will be crucial, requiring a strong understanding of best practices for designing modular and reusable data models, such as star and snowflake schemas.
• • You will apply your expertise in prompt engineering and design for Large Language Models (LLMs) like GPT, focusing on creating, refining, and optimizing prompts to enhance response accuracy, relevance, and performance for internal tools and use cases.
• • Advanced SQL proficiency is essential for complex data transformation, querying, and optimization tasks.
• • You will utilize intermediate to advanced Python skills for scripting, automation, Object-Oriented Programming (OOP), and building scalable frameworks.
• • Strong collaboration and communication skills are vital for translating technical concepts into business value for diverse cross-functional stakeholders and managing projects effectively.
• • Experience in data pipeline development, including building, maintaining, and optimizing ETL/ELT pipelines using modern tools like dbt or Airflow, is expected.
• • Proficiency in building polished dashboards using visualization tools such as Looker, Tableau, Superset, or Python libraries like Matplotlib and Plotly will be necessary.
• • Familiarity with development tools like version control (GitHub) and CI/CD practices, along with modern development workflows, is important.
• • Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks will be beneficial.
• • A solid understanding of business acumen is required to effectively address business challenges through analytics engineering.
• • Familiarity with statistics and probability will be leveraged to ensure data-driven insights are sound.
• • Bonus skills include experience with cloud platforms like AWS or GCP, and familiarity with containerization technologies like Docker or Kubernetes.