Data Engineer (AWS / Redshift ) Consolidate Multi-Source Data

Overview

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.