Analytical Engineer

Job Description

What Youll Build & Run Approximate Focus: 60% API / 40% Data Focus Area Typical Work Product-Facing APIs Design REST/GraphQL endpoints for cohort, feature-flag, and release-notes data. Build microservices in Java/Kotlin (Spring Boot or Vert.x) or Python (FastAPI) with production-grade SLAs. Schema & Contract Management Manage JSON/Avro/Protobuf schemas, generate client SDKs, and enforce compatibility through CI/CD pipelines. Data-Ops Integration Interface with Delta Lake tables in Databricks using Spark/JDBC. Transform datasets with PySpark or Spark SQL and surface them via APIs. Pipeline Stewardship Extend Airflow 2.x DAGs (Python), orchestrate upstream Spark jobs, and manage downstream triggers. Develop custom operators as needed. DevOps & Quality Manage GitHub Actions, Docker containers, Kubernetes manifests, and Datadog dashboards to ensure service reliability. LLM & AI Features Enable prompt engineering and embeddings exposure via APIs; experiment with tools like OpenAI, LangChain, or LangChain4j to support product innovation. About You You’re a language-flexible engineer with a solid grasp of system design and the discipline to ship robust, well-documented, and observable software. You’re curious, driven, and passionate about building infrastructure that scales with evolving product needs. Mandatory Skills 4 to 6 years of professional experience in Java (11+) and Spring Boot Solid command of API design principles (REST, OpenAPI, GraphQL) Proficiency in SQL databases Experience with Docker, Git, and JUnit Hands-on knowledge of low-level design (LLD) and system design fundamentals Highly Preferred / Optional Skills Working experience with Apache Airflow Familiarity with cloud deployment (e.g., Azure AKS, GCP, AWS) Exposure to Kubernetes and microservice orchestration Frontend/UI experience in any modern framework (e.g., React, Angular) Experience with Python (FastAPI, Flask) Good-to-Have Skills CI/CD pipeline development using GitHub Actions Familiarity with code reviews, HLD, and architectural discussions Experience integrating with LLM APIs like OpenAI and building prompt-based systems Exposure to schema validation tools such as Pydantic, Jackson, Protobuf Monitoring and alerting with Datadog, Prometheus, or equivalent