Technology partner certifications: Work through structured certification programs with our technology partners across martech platforms and marketing data tools, building real working proficiency rather than surface-level exposure.
Data querying and structuring: Get hands-on with SQL and structured data work, writing queries, understanding joins and aggregations, and learning how marketing data is organized and modeled.
Pipeline and integration exposure: Build working familiarity with how marketing data moves between systems, covering pipeline concepts, API-based integration, and tool categories like data warehouses and reverse-ETL.
Scripting for data work: Use Python or similar scripting to clean, transform, and work with marketing data, learning tools like pandas as you go.
Data quality practice: Learn to spot and fix data quality issues, inconsistent records, broken mappings, and gaps in how data flows into the places marketing teams use it.
Market and use-case research:
Investigate open problems and emerging use cases across the martech and data landscape to help our team understand what's possible and where the gaps are.
Solution curation support: Help shape and evaluate the solutions and services we consider bringing to clients, based on your research and certification work.
Writing and content contribution: Turn your research into written output, internal notes that inform our thinking, and external content such as newsletters, articles, and whitepapers.
Guided exposure to client work: You won't be placed directly on client engagements at this stage, but you'll get a real glimpse of client problems as you assist the team with research tied to active client challenges.
Qualifications:
The primary profile: technically solid, new to marketing
Pursuing or recently completed a Master's in Computer Science, Data Science, or a related engineering discipline.
Comfortable with structured data and logical problem-solving. Exposure to any
📌 Marketing Data Engineering Intern (Bengaluru)
🏢 Martechture
📍 Bengaluru
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