Analytics Engineer II
Description
- Reliability: Data is delivered on time and consistently, with minimal disruptions that impact end users.
- Analytics Readiness: Analytics infrastructure and platforms are well‑designed, accessible, and easy to use, enabling teams to answer questions quickly with fewer data issues or downtime.
- Impact: Infrastructure supports meaningful business decisions and AI initiatives, demonstrated by increased use of trusted datasets and semantic layers, faster delivery of insights, and improved analytics.
Major Duties Design, build, and maintain SQL/dbt data models and semantic layers optimized for analytical query patterns and AI agent access. Define and maintain semantic layer encoding business logic, KPIs, and metric definitions in a single authoritative location. Apply dimensional modeling techniques to accurately represent business processes and metrics in our data warehouse. Prepare and curate data assets optimized for machine learning, LLM, and AI-agent workflows. Partner with analysts, data scientists, and stakeholders to translate business requirements into trusted data/analytical products. Develop and optimize ELT/ETL pipelines in a cloud data warehouse (e.g., Snowflake) for reliable, scalable data delivery. Implement data quality testing (schema, freshness, business rules) and remediate recurring data issues. Establish and sustain data lineage standards across analytical models, enabling stakeholders to trace data provenance from source to consumption in support of data governance, auditability, and operational reliability. Contribute to shared analytics codebases via Git/GitHub, including code reviews and established development workflows. Support Python-based automation, lightweight analytics services, and troubleshooting of cloud or containerized components as needed. |
Knowledge/Skills: Core Analytics Engineering Skills
Data Platforms & Infrastructure
Data Platforms & Orchestration
Version Control & Development Practices
Additional Knowledge/Skills:
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Education/Experience: Bachelor’s degree in computer science or related field and three to five years of software development or related experience. Preference will be given to those with experience or knowledge in AI-related processes and technologies, including Python, DBT, Azure, SQL Server, and Snowflake. Education and experience equivalencies will be considered. |
- Collaborative: We convene our partners to achieve solutions.
- Productive: We get the right things done.
- Respectful: We treat one another with dignity and civility.
- Bold: We aspire to transform.
- Inclusive: We nurture a culture of belonging.
- Creative: We color outside the lines.
- A monthly employer contribution towards benefits
- A monthly employer match for a health savings account
- An impressive 17% employer contribution to retirement plan (based on qualified earnings)
- A generous Paid Time Off (PTO) program
- Two exceptional employee discount programs