Senior Data Engineer

Publicis Media

Greater London

Hybrid

GBP 55,000 - 95,000

Full time

6 days ago

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Benefits offered by this job

Pension

Life assurance

Private medical

Work Abroad option

Reflection days

Help at Hand

Family-friendly policies

Flexible working

Bank holiday swap

Birthday leave

Job summary

Publicis Media is seeking a hands-on Data Engineer to shape our cloud-native data architecture. You will build and optimize ingestion and transformation pipelines, unify datasets across 20+ marketing platforms, and contribute to production codebases in Python, SQL, and Spark.

You will collaborate with product and data science teams, implement CI/CD and data governance, and mentor junior engineers while ensuring performance and reliability across pipelines.

Qualifications

Hands-on experience as a Data Engineer or Data Platform Developer, with contributions to production codebases.
Expert-level 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 scale.
Strong experience with AWS or GCP (S3, Redshift, Glue, Lambda; BigQuery, Dataflow).
Experience with marketing/ad platform data (Google Ads, Meta, TikTok, DV360, Amazon Ads).
Code versioning (GitHub), CI/CD integration, and data observability practices.
Ability to write clean, modular, testable code and review peers for maintainability and performance.

Responsibilities

Lead hands-on design, coding, and evolution of cloud-native data architecture (AWS/GCP/Snowflake).
Architect and implement scalable ingestion/transformation pipelines using Fivetran, Databricks, and Airflow/DBT.
Design and code modular data services/APIs unifying datasets across 20+ marketing platforms.
Build and optimize schemas, transformations, and ETL logic for high performance.
Contribute to DFP codebase (Python, SQL, Spark) with pipelines and infra.
Establish data versioning, testing, governance ensuring reliability and lineage.
Drive best practices in data modeling, CI/CD, and performance optimization.
Collaborate to define data contracts, schema validation, automated quality checks.
Mentor junior engineers via pair programming, PR reviews, coaching.
Participate in sprint planning/retrospectives to align with platform goals.
Partner with Product/Data Science to translate analysis needs into production-grade solutions.
Design/maintain pipelines for Retrieval-Augmented Generation (RAG) workflows.
Build high-performance APIs and caching for low-latency AI access.
Implement vector retrieval layers (pgVector, Pinecone) and embedding pipelines.
Monitor data latency, cost efficiency, and observability with AI teams.
Work with Platform Operations and Security on privacy and access controls.
Collaborate across analysts/AI engineers to deliver business-critical data products.
Lead documentation, testing, and lineage for reproducibility across pipelines.

Skills

Data Eng

Python

SQL

Spark

ELT Pipelines

Fivetran

Airflow

DBT

Databricks

GitHub

CI/CD

Data Modeling

AWS

GCP

Marketing Data

APIs

Code Review

Tools

Fivetran

Airflow

DBT

Databricks

Python

SQL

Spark

GitHub

Snowflake

Redshift

BigQuery

Job description

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.