Analytical Engineer

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