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Salary: USD 100,000 - 230,000 / annual
InfoSum, WPP's privacy-first data collaboration platform (acquired April 2025 for ~$150M), is seeking a Solutions Engineer to join the North American Client Services team. You will report directly to the Senior Director of Solutions Engineering and serve as a core technical resource for enterprise clients including Disney, NBCU, Netflix, Roku, Walmart, and major WPP brands.
In this hands-on, client-facing role, you will own the complete implementation lifecycle—from pre-sale scoping and proof-of-concept work through post-sale execution, go-live, and ongoing technical support. You will act as the primary technical contact and implementation lead for an assigned portfolio of WPP clients and partners.
Key responsibilities include:
- Scope, design, and execute data collaboration solutions from initial discovery through go-live and optimization
- Write SQL queries and Python scripts to deliver insights and activate campaigns for clients
- Troubleshoot, build, and maintain client implementations across multiple cloud environments (GCP, Snowflake, AWS, Azure, Databricks)
- Support pre-sale technical conversations and solution design alongside the sales team
- Build and maintain technical documentation, implementation playbooks, and process standards as the team scales
- Implement InfoSum's native app technology across all five major cloud environments over the next 6-12 months
- Support growing WPP-internal initiatives including Data Meter (usage tracking), Data Catalog (asset discovery), and Dataset Audit (data validation)
You will work with some of the world's most recognizable brands and serve as a "customer-facing DevOps resource" during the post-sale cycle, combining technical depth with strong client communication.
REQUIREMENTS:
- SQL proficiency (ESSENTIAL): Must be comfortable writing, debugging, and optimizing SQL queries. InfoSum's platform is built on a SQL-like query language.
- Python for data operations: Hands-on experience using Python to format data, create deliverables, and automate workflows. Must have actually coded in Python—not just AI-assisted familiarity.
- Cloud environment experience: Experience working in at least one of GCP, Snowflake, AWS, Azure, or Databricks. Comfortable navigating cloud storage and understanding cloud architecture concepts.
- Data engineering or analytics background: Prior experience in data engineering, analytics, or a similar technical role working with structured data and ETL/ELT processes.
- Client-facing technical communication: Ability to translate complex technical concepts for non-technical stakeholders and document solutions clearly.
- Problem-solving mindset: Comfortable troubleshooting issues across distributed systems and cloud environments.
