Remote | Data Pipeline & Analytics Engineering Consultant — $95–$135/hour
About The Position
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.
Requirements
- 3+ years of experience in data engineering, analytics engineering, data platform engineering, BI engineering, warehouse engineering, or related technical roles
- Experience with one or more areas such as dbt model development, ETL/ELT pipelines, orchestration, data quality testing, warehouse design, schema documentation, incremental models, or data contracts
- Familiarity with tools and platforms such as dbt, Airflow, Dagster, Prefect, Snowflake, BigQuery, Redshift, Databricks, Spark, SQL, Python, or similar data engineering systems
- Comfort reading and preparing data engineering artifacts such as dbt models, DAGs, schema docs, data contracts, test suites, pipeline documentation, and warehouse diagrams
- Strong written communication skills and ability to explain data engineering reasoning clearly
- Ability to follow structured instructions and produce evidence-based work
Nice To Haves
- Experience with dbt model development, data contracts, incremental models, data lineage, orchestration frameworks, or modern data stack workflows
- Familiarity with Snowflake, BigQuery, Redshift, Databricks, Spark, SQL optimization, Python-based pipelines, or cloud data platforms
- Experience preparing or reviewing DAGs, schema documentation, data quality tests, transformation logic, warehouse models, or pipeline runbooks
- Familiarity with CI/CD for data pipelines, data observability, testing frameworks, or performance tuning
- Strong attention to detail in code-heavy, data-heavy, and documentation-based technical environments
Responsibilities
- 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
- 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
- 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
Benefits
- Flexible, project-based assignments
- Competitive hourly compensation
- Weekly payments via Stripe or Wise
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What This Job Offers
Job Type
Part-time
Career Level
Mid Level
Education Level
Associate degree
