Senior Data Engineer

Salary: £55,000 - 95,000 per year

Requirements

• Hands-on experience as a Data Engineer or Data Platform Developer, including direct contributions to production codebases.
• Expert-level proficiency in Python, SQL, and Apache Spark for data pipeline development.
• Proven experience building ELT workflows using Fivetran, Airflow, DBT, or Databricks.
• Solid understanding of data modeling, dimensional design, and schema normalization at enterprise scale.
• Strong experience with AWS (S3, Redshift, Glue, Lambda) or GCP (BigQuery, Dataflow).
• Experience working with marketing or ad platform data (Google Ads, Meta, TikTok, DV360, Amazon Ads).
• Demonstrated expertise in code versioning (GitHub), CI/CD integration, and data observability practices.
• Ability to write clean, modular, testable code and review peers contributions for maintainability and performance.

Responsibilities

• Lead the hands-on design, coding, and evolution of our cloud-native data architecture (AWS/GCP/Snowflake).
• Architect and implement scalable ingestion and transformation pipelines using Fivetran, Databricks, and Airflow/DBT.
• Design and code modular data services and APIs that unify campaign, conversion, and audience datasets across 20+ marketing platforms.
• Build and optimize schemas, transformations, and ETL logic for high-performance, reusable data workflows.
• Contribute directly to our DFP codebase (Python, SQL, Spark), developing pipelines and infrastructure alongside the engineering team.
• Establish data versioning, testing, and governance frameworks ensuring reliability, lineage, and compliance.
• Drive best practices in data modeling, CI/CD, code reviews, and performance optimization.
• Collaborate across engineering teams to define data contracts, schema validation rules, and automated quality checks.
• Mentor junior engineers by pairing on code, reviewing pull requests, and helping them deepen technical maturity.
• Participate actively in sprint planning and retrospectives, ensuring engineering execution aligns with platform goals.
• Partner with Product and Data Science teams to translate analytical requirements into efficient, production-grade data solutions.
• Design and maintain pipelines supporting Retrieval-Augmented Generation (RAG) and Context Engine workflows.
• Build high-performance APIs and caching strategies enabling low-latency data access for AI agents and orchestration systems.
• Implement vector-based data retrieval layers (pgVector, Pinecone) and ensure efficient embedding pipelines for AI contexts.
• Partner with AI teams to monitor data latency, cost efficiency, and observability metrics.
• Partner with Platform Operations and Security to enforce privacy, compliance, and access control frameworks.
• Work cross-functionally with analysts, AI engineers, and platform leads to deliver production-grade, business-critical data products.
• Lead by example in data documentation, pipeline testing, and lineage tracking, ensuring transparency and reproducibility across all pipelines.

Technologies

• AI
• AI Agents
• Airflow
• AWS
• Lambda
• Redshift
• Architect
• BigQuery
• CI/CD
• Cloud
• Databricks
• ETL
• Fivetran
• GCP
• GitHub
• Marketing
• Python
• RAG
• SQL
• Security
• Snowflake
• Spark
• dbt
• Support
• REST

More

• pension
• life assurance
• private medical and income protection
• Work Your World (up to 6 weeks a year working anywhere with a Publicis office)
• Reflection Days
• Help@Hand benefits
• family-friendly policies
• flexible working
• bank holiday swap
• a birthday day off

We operate a hybrid working pattern with full-time employees office-based three days per week.

last updated 37 week of 2026