Remote Data Pipeline & Analytics Engineering Consultant - 95- 135 hour

We are sharing a specialised part-time consulting opportunity for professionals experienced in data engineering, analytics engineering, ETL/ELT workflows, pipeline orchestration, data quality testing, warehouse design, and structured technical documentation processes.

This role supports current and upcoming remote consulting opportunities focused on structured data pipeline review, analytics engineering workflow analysis, orchestration assessment, data quality validation, warehouse documentation, and high-quality project execution. Selected professionals will apply their data engineering expertise to review realistic pipeline scenarios, evaluate technical requirements, prepare structured written outputs, and support accurate, evidence-based data workflow tasks.

Key Responsibilities

Professionals in this role may contribute to:

Pipeline Development & ETL/ELT Review

• Review data engineering scenarios involving ETL/ELT pipelines, dbt models, incremental logic, watermark behavior, transformations, and output tables
• Evaluate pipeline outputs against defined data contracts, expected table structures, source materials, and transformation requirements
• Support structured review of SQL models, dbt projects, pipeline documentation, transformation logic, and data processing workflows
• Identify missing logic, incorrect transformations, schema issues, and expected pipeline outcomes

Orchestration, Testing & Data Quality

• Review orchestration scenarios involving Airflow, Dagster, Prefect, scheduled jobs, DAG dependencies, retries, and workflow execution
• Evaluate data quality tests against known pass/fail cases, validation rules, test suites, and documented expectations
• Support structured review of data quality checks, pipeline test cases, orchestration documentation, and monitoring workflows
• Prepare clear written explanations for data engineering decisions based on source materials and verifiable criteria

Warehouse Design & Data Contracts

• Review warehouse design scenarios involving schemas, data models, performance targets, query-time budgets, partitioning, clustering, and storage design
• Evaluate schema designs against defined contracts, downstream requirements, performance expectations, and documented constraints
• Support structured review of data contracts, schema documentation, warehouse models, and analytics engineering artifacts
• Maintain accuracy, consistency, and professional judgment across submitted work

Ideal Profile

Strong candidates may have:

• 3+ years of experience in data engineering, analytics engineering, data platform engineering, BI e