Data Platform Engineer — Real-Time Atlas CDP & Pipelines

AKA Group Limited

Greater London

Hybrid

GBP 70,000 - 110,000

Full time

14 days+

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Benefits offered by this job

25 days holiday

Telus Health EAP for you and family

Holiday Buy Back

Gym Discounts

Theatre tickets for friends & family

New Business Incentives

Pension Scheme

Ride to Work Scheme

Childcare Vouchers

Death in Service

Flexible/Hybrid Working

Critical Illness

Enhanced Parental Leave

Job summary

AKA Group Limited, the UK's leading entertainment marketing and advertising agency, is seeking a Data Engineer to join the Data & Insights Team. You will own pipelines, identity resolution, and ETL/ELT workflows to deliver a real-time CDP platform from data sources to activation and reporting.

You will collaborate with data scientists and developers, implement CI/CD for data and ML workflows, and use AI-assisted tools to accelerate development while maintaining governance and reliability in a

Qualifications

Degree in Computer Science or related field or equivalent professional experience.
3+ years' experience as a Data Engineer, with proficiency in Python for building data pipelines.
Experience with dbt or equivalent transformation frameworks, and ETL/ELT tools such as Fivetran or Airbyte.
Experience designing and operating production data pipelines with CI/CD for data/ML workflows.
Strong engineering fundamentals: version control, testing, monitoring and documentation.
Comfortable working with AI coding assistants as part of standard development workflow.

Responsibilities

Lead engineering delivery of Atlas: event streaming infrastructure, schema and taxonomy standards, Snowflake data warehouse and dbt pipelines.
Design and build production identity resolution pipelines with Data Scientist to translate matching logic into robust production code.
Own ETL/ELT and reverse-ETL orchestration ensuring reliable data flow into the warehouse as volume increases.
Build and maintain CI/CD pipelines and engineering practices supporting Atlas modelling, forecasting and MMM automation.
Support productionisation of propensity, forecasting and MMM outputs built by the Data Scientist, ensuring reliability at scale.
Implement data governance, monitoring, alerting and incident runbooks for a real-time platform underpinning live client reporting.
Use AI-assisted development tools as part of day-to-day engineering practice to accelerate build, testing and documentation.
Translate engineering constraints for non-technical stakeholders with Account Management and client teams.
Contribute to phase-gated delivery milestones and broader Data & Insights infrastructure beyond Atlas.
Adhere to Data Engineering team standards and documentation across client data pipelines.