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.
Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery.
Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly).
Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.

Requirements

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.
Bonus Skills: Experience with cloud platforms (e.g., AWS, GCP).
Familiarity with Docker or Kubernetes.

Benefits

Health insurance
401(k) matching
Bonus eligibility
Equity eligibility

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data modelingadvanced SQLPythonETLELTdata visualizationprompt engineeringdata pipeline developmentdata architecturestatistics

Soft Skills

collaborationcommunicationproject managementbusiness acumenstakeholder engagement