We’re looking for an experienced data engineer or analytics specialist to help us bring multiple existing data sources together into a single, well-structured Amazon Redshift environment.
The goal is to set up a reliable data foundation so our team can run analytics, reporting, and future AI models from one clean source of truth.
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Data Sources to Integrate
You’ll be connecting and consolidating data from: • Aurora (MySQL) – transactions, users • HelpScout – customer service tickets • Mailchimp – email campaigns & engagement • Meta Ads (Facebook Marketing API) – campaign performance, spend, ROAS • Google Analytics 4 (GA4) – web sessions & conversions
All data should flow into Amazon Redshift Serverless, using S3 as the raw landing zone.
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Deliverables 1. Data Warehouse Setup • Configure Redshift + S3 structure (raw, staging, analytics schemas) • Set up secure, automated access and permissions 2. ETL / ELT Pipelines • Build or configure ingestion pipelines (Airbyte, Fivetran, or custom Python/Lambda) • Create transformations (SQL / dbt) for clean, analytics-ready tables 3. Data Modeling • Standardize user IDs and timestamps across all sources • Produce core joined tables (users, orders, engagement, campaigns, support) 4. Documentation • Schema diagram, data dictionary, and connection details • Clear handover instructions for internal use 5. Validation • Sample dashboards or queries to confirm the data joins correctly
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Required Skills • Strong experience with AWS Redshift, S3, and SQL • Hands-on with ETL tools (Airbyte, Fivetran, Stitch, or custom scripts) • Familiarity with API integrations (Mailchimp, Meta Ads, GA4, HelpScout) • Knowledge of dbt or similar for transformation and testing • Good communication and documentation skills
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Nice to Have • Experience with AWS Glue / Lambda / Step Functions • Understanding of marketing analytics (attribution, ROAS, LTV)
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Project Details • Location: Remote (UK / EU time zone preferred) • Start: Immediate • Duration: Approx. 4–6 weeks, with potential for follow-up work
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To Apply
Please include: • Short summary of similar AWS/Redshift projects • Preferred ETL tool or stack • Example of a data model or architecture you’ve built (no sensitive info)
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✅ Objective: Deliver a working Redshift data warehouse with automated pipelines, consolidated datasets, and clear documentation — ready for analytics and AI use.
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