Data Engineer – Consumer Goods – LATAM Day rate: £150 - £300 Duration: 1 – 3 months Start: ASAP My new client in the consumer goods sector are embarking on an exciting project, focusing on analysing marketing data.
This project integrates data from various sources, like Adverity, campaign briefs, and marketing reports.
We aim to build a robust data infrastructure that will enable weekly analysis of campaign performance, audience segmentation, and ROI calculation.
The pilot is designed to be scalable, with plans to extend it to other brands and integrate more data.
They are looking for a Data Engineer to design, implement, and maintain the data infrastructure for this innovative marketing analytics project.
The ideal candidate will have strong skills in data integration, warehousing, and processing, with the ability to work on a standalone system that will be the foundation for future expansion.
Primary Responsibilities Data Source Integration Set up and maintain connectors for various data sources, with a primary focus on Adverity integration Develop and optimize data extraction and ingestion pipelines Implement data transformation and cleaning processes for marketing data Data Warehousing Design and implement a scalable data warehouse schema suitable for marketing analytics Set up efficient ETL/ELT processes for weekly data loading Develop data partitioning and indexing strategies for optimal query performance Data Quality and Governance Implement comprehensive data quality checks and validation rules Establish data lineage tracking systems Develop and enforce data governance policies in line with UK regulations Analytics Support Collaborate with data analysts to understand and support their data needs Optimize data models for campaign performance analysis, audience segmentation, and ROI calculations Develop and maintain data pipelines for generating weekly insights System Architecture Design and implement a modular, scalable architecture that can expand to other brands and countries Ensure the system can handle increasing data volumes and complexity over time Qualifications Strong programming skills in Python Extensive experience with SQL and data warehousing concepts Proficiency in designing and implementing ETL/ELT processes Preferred Skills Experience with cloud platforms (AWS, GCP, or Azure) Knowledge of data governance and compliance requirements Basic understanding of DevOps practices and tools English speaking Technologies While we're open to various technology solutions, experience with some of the following is beneficial: Data Integration: Apache Airflow, Talend, or similar ETL tools Data Warehousing: Snowflake, Amazon Redshift, or similar Data Quality: Great Expectations, Deequ, or similar Data Processing: Apache Spark, dbt, or similar Version Control: Git We encourage candidates to bring their expertise and suggest optimal solutions for our needs.
Nice-to-Have DevOps Skills While not required, familiarity with the following DevOps practices would be beneficial: Infrastructure as Code (e.g., Terraform, CloudFormation) Containerization (e.g., Docker) CI/CD pipelines Monitoring and logging systems