Senior Data Engineer (AI Consumer Intelligence Platform)

We are a rapidly growing consumer intelligence platform used by leading global brands such as Coca-Cola, Unilever, and Bayer.

• 5+ years building and maintaining production ETL/ELT data pipelines
• 2+ years working with text/NLP data including tokenization, embeddings, and multilingual processing
• 3+ years of shipping data products to production serving active business users
• 4+ years production experience with Python (required)
• 2+ years with PySpark or 1+ years with Spark plus strong Python skills
• 3+ years with SQL including complex queries and performance optimization
• 1+ years production use of Databricks, including notebooks, Delta Lake, and job scheduling
• 2+ years experience with cloud platforms, preferably Azure
• Experience deploying containerized applications to Kubernetes
• Bachelor's degree in Computer Science, Data Science, Engineering, or related quantitative field

Nice to have:

• Experience with Databricks Delta Lake architecture and Unity Catalog
• Deep knowledge of Azure ecosystem including Data Factory, Databricks, Blob Storage, DevOps
• Experience integrating LLM APIs such as OpenAI, Anthropic, or Azure OpenAI in production
• Experience with LLM fine-tuning
• Familiarity with observability tools like DataDog, Grafana, or Azure Monitor
• Experience processing 1B+ records at scale
• Multilingual text processing experience with 3+ non-English languages
• Familiarity with NLP libraries such as Spacy, NLTK, CoreNLP, and TextBlob
• Experience in consumer insights, CPG/FMCG, or advertising technology domains
• Experience working in early-stage, high-growth companies

• Design, build, and maintain production data pipelines processing 10M+ text records daily across multiple languages • Architect scalable NLP data infrastructure using PySpark, Databricks, and Azure services • Integrate Large Language Model APIs into production pipelines for text analysis and enrichment • Establish DataOps standards including CI/CD, testing frameworks, and deployment automation • Implement observability and alerting for pipeline health, data quality, and system performance • Collaborate with data scientists to productionize ML models and NLP systems • Define data governance frameworks and quality SLAs for enterprise client delivery • Mentor team members and contribute to technical hiring as the team scales

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