Senior Manager, Analytics Engineering
Coinbase
Senior Manager, Analytics Engineering
Job Description
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.
Required Qualifications
- 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.
- 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.
- 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.
Required Skills
- hiring and managing data teams
- problem solving
- LLMs and AI in analytics
- prompt engineering
- data modeling
- ETL/ELT
- modern data stack tools (Airflow, DBT, Snowflake, Hex)
- version control (GitHub)
- CI/CD
- modern development workflows
- OOP
- building scalable frameworks
- advanced SQL
- statistics and probability
- CX and Compliance analytics
Benefits
- bonus eligibility
- equity eligibility
- benefits (including medical, dental, vision and 401(k))
Salary Range
$243865 - $286900 USD
Locations
- US Zone 1 (Job Requisitions Only), United States (Remote)
Salary
243,865 - 286,900 USD / yearly
Skills Required
- hiring and managing data teamsintermediate
- problem solvingintermediate
- LLMs and AI in analyticsintermediate
- prompt engineeringintermediate
- data modelingintermediate
- ETL/ELTintermediate
- modern data stack tools (Airflow, DBT, Snowflake, Hex)intermediate
- version control (GitHub)intermediate
- CI/CDintermediate
- modern development workflowsintermediate
- OOPintermediate
- building scalable frameworksintermediate
- advanced SQLintermediate
- statistics and probabilityintermediate
- CX and Compliance analyticsintermediate
Required Qualifications
- Experienced in building and leading teams: You have experience hiring and managing data teams (including managers), and know how to inspire and grow talent. (experience)
- Relentless problem solver: You thrive on tackling new and complex challenges, even those outside of your expertise. (experience)
- 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. (experience)
- Hands-on tech lead: You’re comfortable balancing hands-on work with strategic leadership. (experience)
- Data modeling and tools: You’re an expert in data modeling, ETL/ELT, and modern data stack tools (e.g., Airflow, DBT, Snowflake, Hex). (experience)
- 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. (experience)
- Autonomous and accountable: You operate with a high degree of independence while taking full ownership of outcomes. (experience)
- 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. (experience)
- 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. (experience)
- 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)
- 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. (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.
Benefits
- general: bonus eligibility
- general: equity eligibility
- general: benefits (including medical, dental, vision and 401(k))
Tags & Categories
Senior Manager, Analytics Engineering
Coinbase
Senior Manager, Analytics Engineering
Job Description
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.
Required Qualifications
- 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.
- 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.
- 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.
Required Skills
- hiring and managing data teams
- problem solving
- LLMs and AI in analytics
- prompt engineering
- data modeling
- ETL/ELT
- modern data stack tools (Airflow, DBT, Snowflake, Hex)
- version control (GitHub)
- CI/CD
- modern development workflows
- OOP
- building scalable frameworks
- advanced SQL
- statistics and probability
- CX and Compliance analytics
Benefits
- bonus eligibility
- equity eligibility
- benefits (including medical, dental, vision and 401(k))
Salary Range
$243865 - $286900 USD
Locations
- US Zone 1 (Job Requisitions Only), United States (Remote)
Salary
243,865 - 286,900 USD / yearly
Skills Required
- hiring and managing data teamsintermediate
- problem solvingintermediate
- LLMs and AI in analyticsintermediate
- prompt engineeringintermediate
- data modelingintermediate
- ETL/ELTintermediate
- modern data stack tools (Airflow, DBT, Snowflake, Hex)intermediate
- version control (GitHub)intermediate
- CI/CDintermediate
- modern development workflowsintermediate
- OOPintermediate
- building scalable frameworksintermediate
- advanced SQLintermediate
- statistics and probabilityintermediate
- CX and Compliance analyticsintermediate
Required Qualifications
- Experienced in building and leading teams: You have experience hiring and managing data teams (including managers), and know how to inspire and grow talent. (experience)
- Relentless problem solver: You thrive on tackling new and complex challenges, even those outside of your expertise. (experience)
- 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. (experience)
- Hands-on tech lead: You’re comfortable balancing hands-on work with strategic leadership. (experience)
- Data modeling and tools: You’re an expert in data modeling, ETL/ELT, and modern data stack tools (e.g., Airflow, DBT, Snowflake, Hex). (experience)
- 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. (experience)
- Autonomous and accountable: You operate with a high degree of independence while taking full ownership of outcomes. (experience)
- 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. (experience)
- 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. (experience)
- 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)
- 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. (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.
Benefits
- general: bonus eligibility
- general: equity eligibility
- general: benefits (including medical, dental, vision and 401(k))
Tags & Categories