Principal Data Engineer

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market. What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture. Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform. Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins – big and small. Supported by operating principles of being strategy-led, values -based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together! About the Role We're seeking a versatile Principal Data Engineer who can work across the full stack of Anaplan AI applications, from model integration, prompt engineering and set the technical direction for how we ingest, transform, store, serve, and govern the data that powers our LLM-based and agentic systems. You'll build AI features that can be used in real-time. This will help business users use GenAI in their planning workflows. You'll need both deep knowledge of machine learning and strong data engineering skills. Your Impact Design and build the retrieval layer powering RAG and agentic workloads—including vector and graph databases, hybrid search, and architecting knowledge graphs that capture the semantics of customer models. Develop end-to-end GenAI features including backend API services, model integration, model monitoring, evaluations and deployments Engineer feature and context pipelines balancing batch and streaming patterns to feed forecasting and anomaly-detection models, collaborating closely with data scientists to productionise algorithms. Build the data plane for evaluation , implementing rigorous frameworks to continuously monitor, measure, and improve GenAI feature quality, accuracy, latency, and user satisfaction. , Collaborate with data scientists to productionise ML models and forecasting algorithms Your Skills Extensive background in Data Science Engineering, with a clear track record of principal-level technical leadership. Hands-on experience building and shipping AI/ML products in production Deep practical experience with LLM-based systems: RAG architectures, embedding pipelines, prompt and response logging, evaluation frameworks. Hands-on expertise with vector databases, graph databases, and knowledge graphs End-to-end exposure in model lifecycle development, including extensive experience training and deploying ML models in production environments. Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns. Proficiency in Python and modern software development practices (testing, code review, CI/CD). Preferred Skills Hands-on experience with cloud-native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Familiarity with Anaplan or similar enterprise planning platforms Experience with A/B testing and experimentation frameworks for AI features Experience with model observability tools (LangSmith, W&B, MLflow) Our Commitment to Diversity, Equity, Inclusion and Belonging (DEIB) We believe attracting and retaining the best talent and fostering an inclusive culture strengthens our business. DEIB improves our workforce, enhances trust with our partners and customers, and drives business success. Build your career in a place where diversity, equity, inclusion and belonging aren’t just words on paper – this is what drives our innova