Senior Data Engineer at Omguk

Omguk

London·1d ago

SeniorFull-timePythonAWSAzureDockerCI/CDGitAI

Salary Range

$99,450 - $134,550 /year

Estimated

This salary is estimated based on similar roles. The actual salary may vary.

About the Role

We are looking for a Lead Data Engineer to join our Marketing Intelligence & Reporting team at Omnicom Media Group. This role involves overseeing data engineering functions and workstreams across global client portfolios, with a focus on designing, deploying, and managing data warehouses on cloud platforms.

Key Responsibilities

→Engage, upskill, and nurture the Data Engineering team to foster a high engagement culture.
→Lead the core design of our data engineering product and data architecture, guiding the team in implementation.
→Provide expertise on cloud-based infrastructure, recommending innovations and best practices.
→Collaborate with the BI team to design standardized data solutions for Annalect EMEA’s clients.
→Lead in designing, building, and maintaining data pipeline architecture for ELT/ETL.
→Explore the use of AI in data engineering solutions.
→Participate in running a community of data engineering specialists across EMEA.
→Contribute to pitch content and scope for new work and solutions.

Requirements

✓5+ years’ experience in data engineering teams, preferably in a global or fast-paced environment.
✓2+ years’ experience leading data engineering teams.
✓5+ years’ experience with modern data platforms in the cloud, with preference for Google Cloud Platform, but Microsoft Azure or AWS also considered.
✓5+ years’ experience in designing and implementing data architecture, ETL/ELT processes, and DevOps pipelines, including Docker and CI/CD.
✓6+ years working with SQL and Python.
✓2+ years working with dbt, using advanced macros and templating.
✓Knowledge of the media industry is essential.
✓Ability to translate business requirements into technical specifications and communicate with stakeholders.
✓Ability to manage Git repositories and use the GitFlow Workflow, preferably through GitLab, and deploy into production environments.