Staff Analytics Engineer
Coinbase
Staff Analytics Engineer
Job Description
Responsibilities
- Be the expert: Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery.
- Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly).
- Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.
- Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization.
- Empower stakeholders by translating business requirements into scalable data models, dashboards, and tools.
- Partner with engineering, data science, product, and business teams to ensure alignment on priorities and data solutions.
- Build frameworks, tools, and workflows that maximize efficiency for data users, while maintaining high standards of data quality and performance.
- Use modern development and analytics tools to deliver value quickly, while ensuring long-term maintainability.
Required Qualifications
- Customer Support Data Experience: Familiarity with data elements and processes supporting successful Customer Support initiatives, including employee performance monitoring, workforce/staffing inputs, and the handling of sensitive PII across a broad stakeholder base.
- Data Modeling Expertise: Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas).
- Prompt Design and Engineering: Expertise in prompt engineering and design for LLMs (e.g., GPT), including creating, refining, and optimizing prompts to improve response accuracy, relevance, and performance for internal tools and use cases.
- Advanced SQL: Proficiency in advanced SQL techniques for data transformation, querying, and optimization.
- Intermediate to Advanced Python: Expertise in scripting and automation, with experience in Object-Oriented Programming (OOP) and building scalable frameworks.
- Collaboration and Communication: Strong ability to translate technical concepts into business value for cross-functional stakeholders. Proven ability to manage projects and communicate effectively across teams.
- Data Pipeline Development: Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar.
- Data Visualization: Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly).
- Development Tools: Familiarity with version control (GitHub), CI/CD, and modern development workflows.
- Data Architecture: Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks.
- Business Acumen: Ability to understand and address business challenges through analytics engineering.
- Data savvy: Familiarity with statistics and probability.
Preferred Qualifications
- Experience with cloud platforms (e.g., AWS, GCP).
- Familiarity with Docker or Kubernetes.
Required Skills
- Customer Support Data Experience
- Data Modeling (star schemas, snowflake schemas)
- Prompt Engineering for LLMs (GPT)
- Advanced SQL
- Intermediate to Advanced Python (OOP, scripting, automation)
- Collaboration and Communication
- Data Pipeline Development (ETL/ELT, dbt, Airflow)
- Data Visualization (Looker, Tableau, Superset, Matplotlib, Plotly)
- Development Tools (GitHub, CI/CD)
- Data Architecture (Snowflake, Databricks)
- Business Acumen
- Statistics and Probability
Benefits
- Bonus eligibility
- Medical
- Dental
- Vision
- 401(k)
Salary Range
$207000 - $244100 USD
Locations
- NAMER, United States (Remote)
Salary
207,000 - 244,100 USD / yearly
Skills Required
- Customer Support Data Experienceintermediate
- Data Modeling (star schemas, snowflake schemas)intermediate
- Prompt Engineering for LLMs (GPT)intermediate
- Advanced SQLintermediate
- Intermediate to Advanced Python (OOP, scripting, automation)intermediate
- Collaboration and Communicationintermediate
- Data Pipeline Development (ETL/ELT, dbt, Airflow)intermediate
- Data Visualization (Looker, Tableau, Superset, Matplotlib, Plotly)intermediate
- Development Tools (GitHub, CI/CD)intermediate
- Data Architecture (Snowflake, Databricks)intermediate
- Business Acumenintermediate
- Statistics and Probabilityintermediate
Required Qualifications
- Customer Support Data Experience: Familiarity with data elements and processes supporting successful Customer Support initiatives, including employee performance monitoring, workforce/staffing inputs, and the handling of sensitive PII across a broad stakeholder base. (experience)
- Data Modeling Expertise: Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas). (experience)
- Prompt Design and Engineering: Expertise in prompt engineering and design for LLMs (e.g., GPT), including creating, refining, and optimizing prompts to improve response accuracy, relevance, and performance for internal tools and use cases. (experience)
- Advanced SQL: Proficiency in advanced SQL techniques for data transformation, querying, and optimization. (experience)
- Intermediate to Advanced Python: Expertise in scripting and automation, with experience in Object-Oriented Programming (OOP) and building scalable frameworks. (experience)
- Collaboration and Communication: Strong ability to translate technical concepts into business value for cross-functional stakeholders. Proven ability to manage projects and communicate effectively across teams. (experience)
- Data Pipeline Development: Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar. (experience)
- Data Visualization: Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly). (experience)
- Development Tools: Familiarity with version control (GitHub), CI/CD, and modern development workflows. (experience)
- Data Architecture: Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks. (experience)
- Business Acumen: Ability to understand and address business challenges through analytics engineering. (experience)
- Data savvy: Familiarity with statistics and probability. (experience)
Preferred Qualifications
- Experience with cloud platforms (e.g., AWS, GCP). (experience)
- Familiarity with Docker or Kubernetes. (experience)
Responsibilities
- Be the expert: Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery.
- Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly).
- Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.
- Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization.
- Empower stakeholders by translating business requirements into scalable data models, dashboards, and tools.
- Partner with engineering, data science, product, and business teams to ensure alignment on priorities and data solutions.
- Build frameworks, tools, and workflows that maximize efficiency for data users, while maintaining high standards of data quality and performance.
- Use modern development and analytics tools to deliver value quickly, while ensuring long-term maintainability.
Benefits
- general: Bonus eligibility
- general: Medical
- general: Dental
- general: Vision
- general: 401(k)
Tags & Categories
Staff Analytics Engineer
Coinbase
Staff Analytics Engineer
Job Description
Responsibilities
- Be the expert: Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery.
- Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly).
- Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.
- Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization.
- Empower stakeholders by translating business requirements into scalable data models, dashboards, and tools.
- Partner with engineering, data science, product, and business teams to ensure alignment on priorities and data solutions.
- Build frameworks, tools, and workflows that maximize efficiency for data users, while maintaining high standards of data quality and performance.
- Use modern development and analytics tools to deliver value quickly, while ensuring long-term maintainability.
Required Qualifications
- Customer Support Data Experience: Familiarity with data elements and processes supporting successful Customer Support initiatives, including employee performance monitoring, workforce/staffing inputs, and the handling of sensitive PII across a broad stakeholder base.
- Data Modeling Expertise: Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas).
- Prompt Design and Engineering: Expertise in prompt engineering and design for LLMs (e.g., GPT), including creating, refining, and optimizing prompts to improve response accuracy, relevance, and performance for internal tools and use cases.
- Advanced SQL: Proficiency in advanced SQL techniques for data transformation, querying, and optimization.
- Intermediate to Advanced Python: Expertise in scripting and automation, with experience in Object-Oriented Programming (OOP) and building scalable frameworks.
- Collaboration and Communication: Strong ability to translate technical concepts into business value for cross-functional stakeholders. Proven ability to manage projects and communicate effectively across teams.
- Data Pipeline Development: Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar.
- Data Visualization: Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly).
- Development Tools: Familiarity with version control (GitHub), CI/CD, and modern development workflows.
- Data Architecture: Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks.
- Business Acumen: Ability to understand and address business challenges through analytics engineering.
- Data savvy: Familiarity with statistics and probability.
Preferred Qualifications
- Experience with cloud platforms (e.g., AWS, GCP).
- Familiarity with Docker or Kubernetes.
Required Skills
- Customer Support Data Experience
- Data Modeling (star schemas, snowflake schemas)
- Prompt Engineering for LLMs (GPT)
- Advanced SQL
- Intermediate to Advanced Python (OOP, scripting, automation)
- Collaboration and Communication
- Data Pipeline Development (ETL/ELT, dbt, Airflow)
- Data Visualization (Looker, Tableau, Superset, Matplotlib, Plotly)
- Development Tools (GitHub, CI/CD)
- Data Architecture (Snowflake, Databricks)
- Business Acumen
- Statistics and Probability
Benefits
- Bonus eligibility
- Medical
- Dental
- Vision
- 401(k)
Salary Range
$207000 - $244100 USD
Locations
- NAMER, United States (Remote)
Salary
207,000 - 244,100 USD / yearly
Skills Required
- Customer Support Data Experienceintermediate
- Data Modeling (star schemas, snowflake schemas)intermediate
- Prompt Engineering for LLMs (GPT)intermediate
- Advanced SQLintermediate
- Intermediate to Advanced Python (OOP, scripting, automation)intermediate
- Collaboration and Communicationintermediate
- Data Pipeline Development (ETL/ELT, dbt, Airflow)intermediate
- Data Visualization (Looker, Tableau, Superset, Matplotlib, Plotly)intermediate
- Development Tools (GitHub, CI/CD)intermediate
- Data Architecture (Snowflake, Databricks)intermediate
- Business Acumenintermediate
- Statistics and Probabilityintermediate
Required Qualifications
- Customer Support Data Experience: Familiarity with data elements and processes supporting successful Customer Support initiatives, including employee performance monitoring, workforce/staffing inputs, and the handling of sensitive PII across a broad stakeholder base. (experience)
- Data Modeling Expertise: Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas). (experience)
- Prompt Design and Engineering: Expertise in prompt engineering and design for LLMs (e.g., GPT), including creating, refining, and optimizing prompts to improve response accuracy, relevance, and performance for internal tools and use cases. (experience)
- Advanced SQL: Proficiency in advanced SQL techniques for data transformation, querying, and optimization. (experience)
- Intermediate to Advanced Python: Expertise in scripting and automation, with experience in Object-Oriented Programming (OOP) and building scalable frameworks. (experience)
- Collaboration and Communication: Strong ability to translate technical concepts into business value for cross-functional stakeholders. Proven ability to manage projects and communicate effectively across teams. (experience)
- Data Pipeline Development: Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar. (experience)
- Data Visualization: Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly). (experience)
- Development Tools: Familiarity with version control (GitHub), CI/CD, and modern development workflows. (experience)
- Data Architecture: Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks. (experience)
- Business Acumen: Ability to understand and address business challenges through analytics engineering. (experience)
- Data savvy: Familiarity with statistics and probability. (experience)
Preferred Qualifications
- Experience with cloud platforms (e.g., AWS, GCP). (experience)
- Familiarity with Docker or Kubernetes. (experience)
Responsibilities
- Be the expert: Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery.
- Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly).
- Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.
- Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization.
- Empower stakeholders by translating business requirements into scalable data models, dashboards, and tools.
- Partner with engineering, data science, product, and business teams to ensure alignment on priorities and data solutions.
- Build frameworks, tools, and workflows that maximize efficiency for data users, while maintaining high standards of data quality and performance.
- Use modern development and analytics tools to deliver value quickly, while ensuring long-term maintainability.
Benefits
- general: Bonus eligibility
- general: Medical
- general: Dental
- general: Vision
- general: 401(k)
Tags & Categories