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.

Experience:

• 5+ years building and maintaining production ETL/ELT data pipelines
• 2+ years working with text/NLP data (tokenization, embeddings, multilingual processing)
• 3+ years of data products shipped to production serving active business users

Technical Skills:

• 4+ years production experience with Python
• 2+ years with PySpark or Spark plus strong Python background
• 3+ years with SQL including complex queries and performance optimization
• 1+ years production use of Databricks (notebooks, Delta Lake, job scheduling)
• 2+ years with Azure or equivalent cloud platform experience
• Experience deploying containerized applications to Kubernetes

Education:

• Bachelor's degree in Computer Science, Data Science, Engineering, or related quantitative field

Preferred Qualifications:

• Experience with Databricks Delta Lake architecture and Unity Catalog
• Depth in Azure ecosystem: Data Factory, Databricks, Blob Storage, DevOps
• LLM integration experience including OpenAI, Anthropic, or Azure OpenAI API
• LLM fine-tuning experience
• Familiarity with observability tools: DataDog, Grafana, Azure Monitor
• Experience processing 1B+ records at scale
• Multilingual text processing with Unicode and tokenization for 3+ languages
• Familiarity with NLP libraries: Spacy, NLTK, CoreNLP, TextBlob
• Experience in consumer insights, CPG/FMCG, or advertising technology
• Experience at 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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