Lead AI Analytics Engineer

Key Responsibilities

• Analytics Delivery: Personally architect and build Power BI and Superset dashboards, delivering descriptive and prescriptive analytics tied to measurable business outcomes.
• Deep-Dive Analysis & AI-Augmented Insight: 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.
• Data Observability: 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.
• Database & Reporting Platform Ownership: 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.
• Production Support & Capacity Planning: Troubleshoot production data and database issues, plan for capacity upgrades, perform maintenance, monitor performance, and take proactive action to prevent outages.
• Customer & Cross-Functional Collaboration: Partner directly with customers on tailored analytics solutions, and with AI Solutions Consultants, Product, Engineering, and Customer Success to shape the platform roadmap.
• Team Leadership: Build and mentor a high-performing analytics team grounded in ownership and accountability.

Qualifications and Skills

• Experience: 10+ years of hands-on leadership in business intelligence, advanced analytics, or data science, with a track record of personally delivering data-driven solutions.
• Core Technical Depth — SQL, Databases, and Visualization: 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.
• Scale & Database Administration: Proven experience scaling and supporting reports against very large data volumes. Hands-on with Reports configuration; SQL tuning, monitoring setup, and environment sizing recommendations.
• AI, LLM & NLP Fluency: 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.
• Data Observability: Experience setting up or working with data observability practices — monitoring for schema drift, data freshness, volume/quality anomalies — across BI and warehouse layers.
• Platform Breadth: Additional experience with Databricks, Snowflake, or Tableau is a plus.
• Soft Skills: 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.
• Bonus: Experience in process mining, automation intelligence, or value engineering is highly desirable.