We are sharing a specialised part-time consulting opportunity for professionals experienced in data science, analytics engineering, business intelligence, SQL analysis, experimentation, data engineering, and structured data workflow review.
This role supports current and upcoming remote consulting opportunities focused on structured data science review, analytics workflow analysis, business intelligence assessment, experimentation review, data pipeline evaluation, metric documentation, and high-quality project execution. Selected professionals will apply their data and analytics expertise to review realistic technical scenarios, evaluate analytical requirements, prepare structured written outputs, and support accurate, evidence-based data workflow tasks.
Key Responsibilities
Professionals in this role may contribute to:
Analytics, BI & Metric Review
- Review data scenarios involving SQL analysis, ad-hoc business questions, dashboard specifications, metric definitions, funnel analysis, cohort analysis, and reporting outputs
- Evaluate analytical outputs against source data, defined business logic, expected numerical results, and documented requirements
- Support structured review of SQL queries, BI dashboards, dashboard specs, metric documentation, and analytical summaries
- Identify missing assumptions, query issues, metric inconsistencies, reporting gaps, and expected analysis outcomes
Experimentation & Data Science Support
- Review experimentation scenarios involving A/B test design, readouts, lift calculations, statistical significance, guardrail metrics, and decision criteria
- Evaluate experiment outputs against defined metrics, expected values, testing assumptions, and analytical standards
- Support structured review of data science workflows, Python-based analyses, statistical outputs, and business interpretation materials
- Prepare clear written explanations for data science and analytics decisions based on source materials and verifiable criteria
Data Engineering & Pipeline Workflow Review
- Review data engineering scenarios involving ETL/ELT pipelines, dbt models, data quality monitoring, warehouse schema design, Airflow or Dagster DAGs, and pipeline documentation
- Evaluate pipeline outputs, schemas, transformations, orchestration logic, and data quality checks against documented requirements
- Support structured review of data artifacts such as dbt models, schema diagrams, data contracts, test suites, DAGs, and warehouse documentation
- Maintain accuracy, consistency, and professional judgment across submitted work
Ideal Profile
Strong candidates may have: