Senior Data Engineer

Retail Media is transforming how advertisers connect with consumers through personalized and targeted campaigns across retailers' digital and physical touchpoints. Retail Media Measurement plays a pivotal role in ensuring the effectiveness of these campaigns, driving value for advertisers, retailers, and consumers alike.

This role focuses on designing, building, and scaling solutions that enable the accurate measurement of retail media campaigns across various channels. By providing actionable insights, it empowers stakeholders to optimize media investments, improve ROI, and enhance the overall customer experience.

Job Title: Senior Data Engineer Job Summary We are looking for a talented and motivated Senior

Data Engineer

to contribute to the design, development, and optimization of real-time and batch data processing pipelines for our retail media measurement solution. In this role, you will work with tools such as Python, Apache Spark, and streaming frameworks to process and analyze data, supporting near-real-time decision-making for critical business applications in the retail media space. You will collaborate with cross-functional teams, including Data Scientists, Analysts, and Senior Engineers, to build robust and efficient data solutions. As a Data Engineer, you will focus on implementing scalable data pipelines under the guidance of senior team members while gaining hands-on experience with streaming and batch processing systems. Your contributions will help ensure the reliability and performance of our data infrastructure, driving impactful insights for the business. This role offers an excellent opportunity to grow your expertise in modern data engineering practices while working on cutting-edge technologies.

What We Expect From You : Experience:

7–9 years of experience as a Data Engineer. Prior experience working with scalable architecture and distributed data processing systems

Technical Expertise: Strong programming skills in

SQL

and

PySpark

. Proficiency in big data solutions such as

Apache Spark

and

Hive

. Experience with big data workflow orchestrators like Argo Worklfows Hands-on experience with cloud-based data stores like

Redshift

or

BigQuery

(preferred). Familiarity with cloud platforms, preferably

GCP

or

Azure

.

Development Practices: Strong programming skills in

Python

, with experience in frameworks like

FastAPI

or similar API frameworks. Proficiency in unit testing and ensuring code quality. Hands-on experience with version control tools like

Git

.

Optimization & Problem Solving: Ability to analyze complex data pipelines, identify performance bottlenecks, and suggest optimization strategies. Work collaboratively with infrastructure teams to ensure a robust and scalable platform for data science workflows.

Collaboration & Communication: Excellent problem-solving skills and the ability to work effectively in a team environment. Strong communication skills to collaborate across teams and share technical insights.

Nice To Have: Experience with microservices architecture, containerization using

Docker

, and orchestration tools like

Kubernetes

. Exposure to MLOps practices or machine learning workflows using Spark. Understanding of logging, monitoring, and alerting for production-grade big data pipelines.

This role is ideal for someone eager to grow their expertise in modern data engineering practices while contributing to impactful projects in a collaborative environment.