Data Engineer (Mid-level)

The ideal candidate is passionate about building scalable cloud data platforms and solving complex data integration challenges. They have strong SQL and Python skills, experience working with modern cloud technologies, and enjoy developing reliable, maintainable data products that enable analytics across the organisation.

Responsibilities

Design, develop and maintain scalable ELT/ETL pipelines using modern cloud technologies; Build and optimise data ingestion frameworks from first-party, second-party and third-party data sources; Develop and maintain robust data models that support analytics and reporting; Build and manage integrations using REST APIs, SDKs and cloud services; Develop reusable data transformation frameworks using dbt or equivalent tooling; Implement incremental processing, partitioning and performance optimisation techniques; Design and maintain data warehouse structures within BigQuery and other cloud environments; Ensure pipeline reliability through monitoring, alerting and automated testing; Troubleshoot production data issues and implement long-term solutions; Develop scalable cloud-native data solutions on Google Cloud Platform; Optimise BigQuery performance and manage cloud infrastructure costs; Build solutions using Cloud Functions, Cloud Run, Cloud Storage and Pub/Sub where appropriate; Implement secure access controls using IAM principles; Support infrastructure automation using Infrastructure-as-Code where applicable; Monitor platform performance and recommend improvements; Implement automated data validation and quality assurance processes; Develop and maintain metadata, documentation, and data lineage; Support data governance frameworks and taxonomy standards; Ensure compliance with data privacy, security, and governance policies; Establish monitoring for data freshness, completeness, and accuracy; Build curated datasets that enable self-service reporting and analytics; Partner with Analytics Engineers, BI Developers and Data Analysts to deliver trusted datasets; Support dashboard development by providing clean, scalable, and well-modelled data; Develop reusable business logic and transformation layers; Assist with complex data investigations and root cause analysis; Maintain source control using Git; Participate in code reviews and technical design discussions; Develop technical documentation for pipelines, architecture, and integrations; Contribute to CI/CD deployment processes; Continuously improve engineering standards and platform reliability; Research and evaluate emerging cloud and data engineering technologies; Work closely with Media, Analytics, Strategy and Technology teams; Collaborate with platform vendors and technology partners; Support junior engineers through knowledge sharing and mentoring; Contribute to continuous improvement initiatives across the data practice

Requirements

Bachelor's Degree in Computer Science, Information Systems, Engineering, Data Science or a related quantitative discipline; 3–5 years' experience in Data Engineering or Analytics Engineering; Experience developing cloud-based data platforms; Experience building scalable data pipelines; Experience integrating APIs and third-party platforms; Experience working with modern data warehouses; Experience supporting business intelligence and analytics platforms; SQL (Advanced); Python; REST APIs; JSON; Git; dbt; ETL / ELT; Data Modelling; Data Warehousing; Incremental Processing; Pipeline Orchestration; Data Quality Frameworks; Identity Resolution concepts; Google Cloud Platform; BigQuery; Cloud Storage; Cloud Functions; Cloud Run; Pub/Sub; Cloud Composer (Airflow); IAM; Looker; Looker Studio; Power BI; Tableau; Google Analytics 4; Modern Data Architecture; Cloud Computing; Data Governance; Data Privacy; Data Quality; Data Observability; CI/CD Principles; Version Control; Cost Optimisation; Digital Marketing Ecosystem; AI-enabled Data Engineering

Nice to have

Google Cloud Professional Data Engineer certification (preferred); Additional cloud or data engineering certifications; Experience within media, marketing technology or digital analytics; Snowflake; AWS; Azure; Terraform; Docker; Adobe Analytics; Experience integrating or working with data from Google Ads, Campaign Manager 360, DV360, Meta Ads, TikTok, AppsFlyer, SA360, Adverity