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# Data Engineer

- **Company:** SimilarWeb LTD
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Big Data, Cloud Computing, Python (Programming Language), Machine Learning, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Generative AI, Pyspark, Information Technology, HuggingFace, Document Classification, Databricks
- **Published:** September 30, 2026
- **Apply:** https://job-boards.greenhouse.io/similarweb/jobs/8239378

## About the Role

1. Holds a B.Sc. or M.Sc. in Computer Science, Data Science, Mathematics or another relevant field
 2. Has 4+ years of hands-on experience as a data engineer, ML engineer or data scientist, with solutions running in production
 3. Has strong Python skills and writes production-quality code
 4. Has hands-on experience building LLM-based applications in production (prompt engineering, structured outputs, RAG, embeddings, evaluation)
 5. Has worked with the modern LLM stack: LLM provider APIs (OpenAI, Anthropic, etc.), LangGraph or LangChain, Hugging Face and vector stores
 6. Has a solid grasp of text classification and NLP methods, both classical and modern, and knows when to use each
 7. Has experience processing large-scale data with Spark/PySpark, Databricks or similar, on AWS or another cloud
 8. Understands evaluation and data quality well: precision/recall trade-offs, building ground truth, error analysis
 9. Is pragmatic and delivery-focused, comfortable with ambiguity, and communicates clearly with Product and business stakeholders
 10. Has experience with taxonomies, entity resolution or product/e-commerce data (advantage)
 11. Has experience with fine-tuning or deploying open-source models (advantage)

At Similarweb, collaborating with our colleagues in-office creates a more connected, unified culture. Our best work is a product of our face-to-face collaboration, with the ability to work partially from home.

## Description

Your daily responsibilities may include:

 * Designing and building LLM-powered and ML-based pipelines that classify, normalize, structure and match product, brand and category data
 * Building agentic workflows (LangGraph or similar) that automate complex data tasks end to end
 * Choosing the right approach for each problem (LLMs, embeddings, fine-tuned models, classical classifiers or rules), balancing accuracy, cost and latency
 * Scaling solutions to run efficiently over billions of records, using Spark, Databricks and our cloud infrastructure
 * Building evaluation frameworks: ground-truth datasets, labeling processes, quality metrics and ongoing monitoring
 * Taking solutions from POC to production, and owning them after launch
 * Working closely with Product to define requirements and shape the roadmap
 * Collaborating with data engineers, data scientists and other R&D teams on infrastructure and best practices