Data Engineer

Who We Are

Ipsos is one of the world’s largest research companies and currently the only one primarily managed by researchers, ranking as a #1 full-service research organization for four consecutive years.

With over 75 different data-driven solutions, and presence in 90 markets, Ipsos brings together research, implementation, methodological, and subject-matter experts from around the world, combining thematic and technical experts to deliver top-quality research and insights.

Simply speaking, we help the biggest companies solve some of their biggest problems, serving more than 5000 clients across the globe by providing research, data, and insights on their target markets.

About Global Data Management Tower (GDMT)

Ipsos Global Data Management Tower serves as the backbone of Ipsos’ efforts to harness and integrate the power of data, globally.

GDMT is tasked with the critical role of ensuring that data, one of the most valuable assets in the modern digital economy, is accurately collected, stores, processed, and analyzed to support decision-making processes, drive innovation, and maintain Ipsos’ competitive advantage on a global scale.

We’re looking for someone with a deep interest and expertise in data engineering and the dedication to apply that passion.

We need someone with the technical expertise to expand and optimize our existing data pipeline and architecture. The ideal candidate will have experience using cloud-based platforms, like AWS and Google Cloud Services, to build and maintain serverless data pipelines to efficiently process and warehouse large scale datasets.

The role also requires conscientious attention to detail, an ability to work well on a small team, and a self-starter approach to problem solving and debugging.

As a Data Engineer, you will :

• Work with the team to design, code, and test cloud-based data pipelines to process and store large-scale datasets.
• Develop and maintain data warehouses to efficiently store large, complex datasets in a cost-optimal manner.
• Ingest and assemble data from various disparate sources using a variety of tools, including SQL and Python.
• Work with internal / external stakeholders to identify and understand needs and project goals.
• Lead the migration of development code into production, providing technical assistance and guidance to refine and optimize processes.
• Troubleshoot and improve existing infrastructure and codebases.

Requirements :

• Bachelor’s degree (or equivalent) in statistics, computer science, or related field
• Strong technical communication skills
• Proficiency in Python & advanced working knowledge of SQL.
• Experience with collaboration tools (. Atlassian suite) and version control systems (. Git).
• Experience with cloud services such as AWS / Google. Prefer GCP.
• Comfortable working in a highly collaborative, consensus-oriented environment.
• Preferred to have experience collaborating or working as data scientist in the past.
• Familiarity with data science model deployment and maintenance.
• Prior success designing and deploying data pipelines in large-scale production environments.
• Strong eye for detail, ability to communicate scientific concepts both in written form and verbally, and excellent time-management skills.
• Large dataset manipulation. Experience in distributed storage and computing.
• Experience with Linux server and system administration.
• Ability to speak and communicate fluently in English
• Ability to work in EST / PST time zones

If you don’t meet 100% of the requirements, we encourage all who feel they might be a fit for the opportunity to apply. We may consider a variety of backgrounds for a particular role and are also committed to considering candidates for available positions throughout our organization, not just the one you’re applying to!

What’s in it for you :

At Ipsos you’ll experience opportunities for Career Development, an exceptional benefits package (including generous annual leave / paid time off, healthcare plans, wellness benefits), a flexible workplace policy, and a strong collaborative culture.