Browse Skills

Designs dimensional data warehouse schemas using Kimball, Inmon, or Data Vault methodologies. Guides star/snowflake schema design, slowly changing dimensions, ETL/ELT patterns, and cloud warehouse optimization for Snowflake, BigQuery, and Redshift.

Expert guidance for building and optimizing Looker LookML models, Explores, and dashboards. Covers semantic layer design, derived tables, performance optimization, and governance patterns for scalable self-serve analytics.

Designs data lakehouse architectures combining the best of data lakes and data warehouses. Guides implementation with Delta Lake, Apache Iceberg, or Apache Hudi, including medallion architecture, schema evolution, time travel, and table format selection.

Design and optimize manufacturing facility layouts using digital twin simulation. Create virtual replicas of production floors to test layout alternatives, identify bottlenecks, and validate material flow improvements before physical implementation.

Conducts comprehensive bias audits and ethical assessments of AI/ML systems, applying fairness metrics, regulatory frameworks (NIST AI RMF, EU AI Act), and structured impact assessments to identify and mitigate algorithmic harms.

Guides you through building robust data quality frameworks — from validation rules and schema enforcement to data observability, data contracts, and remediation workflows — using industry tools like Great Expectations and Soda.

Designs comprehensive LLM prompt evaluation frameworks with tailored metric suites, automated eval pipelines, A/B testing methodology, red-teaming protocols, and regression detection systems using industry-standard tools like RAGAS, DeepEval, and promptfoo.

Guides analytics engineering best practices using dbt, SQL modeling patterns, semantic layers, metrics definitions, and testing strategies. Helps design maintainable, well-documented analytics workflows from raw data to business-ready datasets.

Create publication-quality static figures with Matplotlib and interactive web-ready visualizations with Plotly, guided by visual perception research and data storytelling principles.

Design and deploy predictive maintenance systems using edge computing, IoT sensor data, and machine learning models for anomaly detection and remaining useful life estimation in industrial environments.

Analyzes and optimizes SQL queries for maximum performance, covering execution plan interpretation, indexing strategies (B-tree/GIN/GiST), window functions, CTEs, materialized views, partitioning, and PostgreSQL-specific optimization techniques.

Provides expert guidance on transformer neural network architectures, including attention mechanisms, positional encoding schemes, encoder-decoder design patterns, tokenization strategies, and context window optimization for building and understanding modern LLMs.

Prepare research outputs for public sharing and long-term preservation with FAIR-compliant data packaging, reproducible environments, proper licensing, and repository publication strategies.

Guides the end-to-end deployment of TensorFlow Lite models on mobile and edge devices — from model conversion and quantization to on-device inference optimization, hardware delegate selection, and production monitoring. Ensures models meet latency, size, and accuracy constraints for resource-constrained environments.

Design interactive maps, choropleths, and spatial data visualizations using best practices in cartographic design, web mapping libraries, and geospatial data engineering.

Designs multi-stage ML model deployment pipelines with automated rollback triggers, canary releases, and shadow deployments to ensure safe, reversible model updates in production.

Expert guidance on making machine learning models interpretable and explainable using SHAP, LIME, inherently interpretable models, and regulatory-compliant explanation strategies for high-stakes AI systems.

Reduces LLM API costs through systematic token optimization — prompt compression, caching strategies, model routing, and batch processing — while maintaining output quality across OpenAI, Anthropic, and open-source models.

Builds and evaluates demand forecasting systems using statistical time series methods and machine learning, covering decomposition, model selection, accuracy measurement, and translation to inventory and planning decisions.

Design and implement MLOps platforms covering model serving, feature stores, CI/CD for ML, model monitoring, drift detection, A/B testing, and GPU resource management.

Guides the design, development, testing, and deployment of custom PostgreSQL extensions using C, Rust (pgrx), or trusted languages (pg_tle) with proper type systems, index integration, and upgrade paths.

Designs 2026-standard RAG systems with agentic retrieval loops, GraphRAG for global reasoning, hybrid search with RRF, supervisor/worker patterns, and evaluation-driven development using RAGAS metrics.

Design and implement automated data quality frameworks using Great Expectations, dbt tests, data contracts, and observability platforms to ensure trustworthy data pipelines.

Design and build reliable API integrations in Make (formerly Integromat) using HTTP modules, OAuth flows, pagination handling, and advanced error recovery patterns.