Senior Analytics Engineer
Location: Remote
Employment Type: Full‑Time
Role Summary
We are seeking a Senior Analytics Engineer to help evolve a business intelligence function from traditional reporting into a modern, proactive, AI‑augmented data platform. This role sits at the intersection of analytics engineering, data infrastructure, and intelligent automation.
You will design and maintain the data models, pipelines, and semantic layers that power both human decision‑making and automated systems. Your work will directly support advanced analytics, AI‑driven workflows, and emerging agent‑based use cases across the organization.
This role is ideal for a senior practitioner who enjoys hands‑on building, thoughtful system design, and influencing technical direction without stepping fully into people management.
Key Responsibilities
Data Infrastructure & Pipelines
- Architect, build, and maintain reliable ELT pipelines using modern data tooling and cloud warehouses.
- Apply AI‑assisted development practices to reduce repetitive work while maintaining high standards for readability and correctness.
- Write efficient, well‑structured SQL and Python to transform, validate, and integrate data from multiple sources.
Data Modeling & Semantics
- Design and maintain analytical data models, including fact tables, dimensions, metrics layers, and reusable datasets.
- Build datasets that serve multiple consumers, including analysts, dashboards, and downstream programmatic or automated workflows.
- Maintain clarity around data ownership, definitions, and relationships.
AI‑Enabled Data Workflows
- Create data structures and pipelines that support AI and LLM‑powered use cases such as context retrieval, embeddings, structured outputs, and tool‑based agents.
- Partner with other technical teams to ensure data is usable, discoverable, and reliable for automated systems.
Reliability, Quality & Observability
- Monitor data freshness, completeness, and correctness across pipelines.
- Surface issues proactively and explore automated alerting or remediation patterns.
- Maintain clear documentation and lineage so both humans and systems can understand how data flows.
Collaboration & Leadership
- Support analytics and reporting tools by ensuring data is performant, documented, and trustworthy.
- Participate in code reviews and planning discussions; help shape longer‑term infrastructure decisions.
- Mentor junior and mid‑level contributors in SQL, data modeling, and effective use of AI development tools.
Required Qualifications
- 7+ years of experience in analytics engineering, data engineering, or a closely related role.
- Strong command of SQL, including complex transformations, performance debugging, and model design.
- Proficiency in Python for data processing, automation, and API integrations.
- Hands-on experience with a cloud data warehouse (Snowflake preferred).
- Experience using dbt or a similar transformation framework.
- Familiarity with orchestration tools (e.g., Dagster or Airflow) or managed ELT services.
- Practical, day‑to‑day use of AI coding assistants as a productivity tool, paired with sound judgment.
- Strong communication skills and a habit of documenting decisions, definitions, and systems.
- Ability to operate as a senior IC—contributing direction, mentoring peers, and supporting planning efforts.
Preferred Experience
- Exposure to LLM‑ or agent‑based systems, such as retrieval‑augmented generation or tool‑driven workflows.
- Experience with BI platforms (e.g., Sigma, Looker, Tableau, Power BI).
- Familiarity with vector search or semantic data infrastructure.
- Experience working in cloud environments (Azure preferred; AWS or GCP also relevant).
- Knowledge of data quality frameworks, testing, and observability tools.
- Comfort working in fast‑moving environments where iteration and learning are critical.