Note: The job is a remote job and is open to candidates in USA. Skan AI is a process intelligence software company building a context engine for human and agentic execution. The Lead AI Analytics Engineer will own the analytics function end-to-end by building dashboards, performing deep-dive analyses with AI-augmented tools, maintaining reliable reporting platforms, supporting production data environments, collaborating with customers and cross-functional teams, and mentoring an analytics team.
Responsibilities
- Personally architect and build Power BI and Superset dashboards, delivering descriptive and prescriptive analytics tied to measurable business outcomes
- Run deep-dive analyses to surface patterns and trends. Use Skan.ai's built-in AI/LLM and NLP capabilities to accelerate analysis of unstructured process and customer data — this is about applying AI tooling to get to insight faster, not building or maintaining models. Partner with the data science team when a question needs model-level depth
- Monitor the health of reporting data pipelines — schema drift, freshness, volume anomalies, and quality checks across Superset/Power BI sources — and escalate before issues reach a customer dashboard
- Configure, tune, and scale the SQL/Superset/Power BI stack for very large data volumes. Set up monitoring and performance metrics, and recommend environment sizing
- Troubleshoot production data and database issues, plan for capacity upgrades, perform maintenance, monitor performance, and take proactive action to prevent outages
- Partner directly with customers on tailored analytics solutions, and with AI Solutions Consultants, Product, Engineering, and Customer Success to shape the platform roadmap
- Build and mentor a high-performing analytics team grounded in ownership and accountability
Skills
- 10+ years of hands-on leadership in business intelligence, advanced analytics, or data science, with a track record of personally delivering data-driven solutions
- Deep, hands-on expertise in SQL, database, and data visualization (Power BI, Superset). Must be technically strong across all three areas, not specialized in just one
- Proven experience scaling and supporting reports against very large data volumes. Hands-on with Reports configuration; SQL tuning, monitoring setup, and environment sizing recommendations
- Comfortable using LLM- and NLP-based tools (extraction, classification, summarization) to speed up data analysis. This is a using-AI-as-a-tool requirement, not a model-building one — deep ML/data science expertise is not expected
- Experience setting up or working with data observability practices — monitoring for schema drift, data freshness, volume/quality anomalies — across BI and warehouse layers
- Highly collaborative and low-ego — equally comfortable pairing with engineers on SQL/DB tuning and walking a customer through a dashboard
- Clear communicator who can translate technical tradeoffs (sizing, performance, risk) into plain language for non-technical stakeholders
- Genuinely curious and eager to learn and grow, with the resilience and ownership mindset to run toward ambiguous production issues rather than waiting to be told what to do
- Experience in process mining, automation intelligence, or value engineering is highly desirable
- Additional experience with Databricks, Snowflake, or Tableau is a plus
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