Browse Skills

Design rigorous geographic experiments (geo-lift tests) to measure the true incremental impact of marketing campaigns, product launches, and pricing changes using causal inference methods.

Designs robust ETL and ELT data pipelines with focus on idempotency, incremental loading, data lineage, and orchestration. Guides implementation with Apache Airflow, dbt, and modern data stack tools.

Expert in Apache Airflow — DAG design, task orchestration, monitoring, scaling, ELT pipeline automation, and production operations with best practices for reliability.

Expert in causal inference methods — causal graphs (DAGs), difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, and policy impact evaluation.

Expert in Python-based data analysis — Pandas, NumPy, exploratory data analysis, statistical profiling, data cleaning, and visualization for business insights.

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, builds, and optimizes lightweight CRM systems in Airtable with proper relational data modeling, pipeline views, automations, and integration patterns for small businesses and agencies.

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.

Designs robust data pipelines covering ETL/ELT patterns, orchestration (Airflow/Dagster/Prefect), data quality validation, lineage tracking, idempotency, streaming vs batch architecture, and data lakehouse design for production workloads.

Design optimal database indexing strategies by analyzing query patterns, understanding index internals, and balancing read performance against write overhead for PostgreSQL, MySQL, and other relational databases.

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.

Maps end-to-end data lineage from source systems through transformations to downstream consumers, enabling impact analysis, debugging data quality issues, and ensuring regulatory compliance.

Expert guidance on real-time stream processing architectures using Apache Kafka, Flink, and Spark Streaming, covering windowing strategies, exactly-once semantics, CEP, and operational best practices.

Designs unified customer profiles using Segment, RudderStack, or custom CDP solutions with identity resolution, event streaming, audience segmentation, and privacy-compliant data governance.

Design and build production-grade real-time data pipelines using Apache Kafka, Flink, Spark Streaming, and CDC patterns with exactly-once semantics and operational observability.

Designs normalized event tracking schemas for product analytics, defining event taxonomies, property standards, and validation rules to ensure consistent data collection across teams.

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.

Designs and implements Monte Carlo simulations for quantitative analysis, covering probability distributions, variance reduction techniques, financial modeling (VaR, options pricing), convergence analysis, sensitivity analysis, and production-quality Python/R implementation patterns.

Designs and implements time series forecasting solutions — from data preprocessing and stationarity testing through model selection (ARIMA, Prophet, LSTM, Transformer-based), evaluation with proper temporal cross-validation (MAPE, RMSE, MASE), to production deployment with ensemble approaches and drift monitoring.

Designs real-time analytics systems using stream processing frameworks like Kafka Streams, Apache Flink, and Spark Streaming. Covers event-time processing, windowing strategies, exactly-once semantics, and low-latency dashboard architectures.

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.

Guides you through designing effective, insight-driven dashboards with proper KPI selection, visual encoding, and data storytelling — applying Tufte and Few principles to avoid common anti-patterns.

Guide systematic exploratory data analysis workflows — from initial profiling through distribution analysis, correlation discovery, and hypothesis generation — using statistical rigor and effective visualization.

Expert analytics engineering skill for dbt Core/Cloud — guides you through SQL-based data modeling with staging/intermediate/mart layers, dimensional modeling (Kimball methodology), incremental models, SCD Type 2 snapshots, comprehensive testing strategies, Jinja macros, dbt packages, and warehouse-specific optimization for Snowflake, BigQuery, and Redshift.