Senior Software Engineer - Semantic Data Lake

4–8 years of experience in data engineering or software engineering with a focus on data transformation, modeling, or analytics platforms.

Strong proficiency in SQL and at least one general-purpose language such as Python or Scala.

Demonstrated experience as an AI-native engineer—using tools like Claude, GitHub Copilot, Cursor, or similar as a regular part of your development workflow.

AI-Native Architecture: You are passionate about the future of data intelligence and have hands-on experience with LLM-driven architectures. You are comfortable designing RAG systems, building agentic workflows (e.g., LangGraph, CrewAI), and working with vector/graph databases to manage context and ontology.

Familiarity with modern AI engineering practices such as prompt design, Spec-Driven Development (SDD), and AI-assisted code review.

Advanced Data Orchestration & Pipeline Reliability: You possess deep expertise in building and managing complex orchestration systems (specifically Airflow) and are obsessed with reliability. You know how to engineer "quality-first" pipelines using frameworks like Great Expectations to ensure data trust at scale.

Infrastructure as Code (IaC) & Platform Engineering: You are fluent in the modern DevOps stack—including Terraform, Kubernetes, and CI/CD automation—allowing you to build self-healing, scalable infrastructure that minimizes operational toil.

Enterprise Governance & Metadata Management: You have a proven track record of implementing automated governance, specifically utilizing DataHub or similar platforms to manage end-to-end lineage, cataloging, and RBAC policies that make security inherent to the data object.

Observability & Incident Management: You understand that "the platform is the product." You bring experience building comprehensive observability frameworks (e.g., Grafana, xMatters integration) that move teams from reactive firefighting to proactive, automated alerting.

Self-Service Mindset: You advocate for "developer autonomy" over manual intervention. You have experience building intuitive portals and developer tools that enable domain teams to build, manage, and consume trusted data products independently.

Solid understanding of data quality practices—including validation, enrichment, schema enforcement, and business rule encoding.

Comfort operating in a collaborative, cross-functional environment, balancing business logic with platform scalability.

A proven track record for traceability, reproducibility, and semantic clarity—you build data models others can trust and reuse.