Staff SW Engineer, DevOps/MLOps
Paradigm Health
AirflowApacheAWSCloudDistributed SystemsDockerETLGoogle Cloud PlatformGrafanaKubernetesPrometheusPython+5 more
Senior Analytics Engineer
Coinbase
full-time
Posted on:
Origin: • 🇺🇸 United States
Visit company websiteJob Level
Senior
Tech Stack
AirflowAWSCloudDockerETLGoogle Cloud PlatformKubernetesPythonSQLTableau
About the role
- The Analytics Engineering team bridges the gap between data engineering, data science, and business analytics by building scalable, impactful data solutions.
- Transform raw data into actionable insights through robust pipelines, well-designed data models, and tools that empower stakeholders across the organization to make data-driven decisions.
- 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.
- 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.
Requirements
- 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.
- Bonus Skills: Experience with cloud platforms (e.g., AWS, GCP). Familiarity with Docker or Kubernetes.
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