Backend Engineer II – Data Platform

Responsibilities

• Architect and implement clean, modular, and scalable backend services using Java, Spring Boot, and modern microservice principles.
• Design efficient database schemas and write optimized queries for RDS (MySQL/PostgreSQL) and, optionally, NoSQL databases like Elasticsearch, MongoDB, or DynamoDB.
• Integrate Kafka or RabbitMQ to build robust and loosely-coupled event-driven architectures.
• Architect and implement scalable, secure, and reliable data pipelines using modern data platforms (e.g., Spark, Databricks, Airflow, Snowflake, etc.).
• Develop ETL/ELT processes to ingest data from various structured and unstructured sources.
• Perform Exploratory Data Analysis (EDA) to uncover trends, validate data integrity, and derive insights that inform data product development and business decisions.
• Collaborate closely with data scientists, analysts, and software engineers to design data models that support high-quality analytics and real-time insights.
• Profile and tune backend performance across databases, APIs, and infrastructure.
• Write clean, maintainable code with comprehensive unit and integration tests to ensure reliability and stability.
• Thrive in an agile, collaborative environment and take ownership of end-to-end feature delivery.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 4+ years of hands-on experience in data engineering or backend software development roles.
• Strong expertise in Java and Spring Boot ecosystem.
• Solid understanding of Relational Databases (RDS, MySQL, PostgreSQL).
• Experience with Apache Kafka or RabbitMQ for building asynchronous, decoupled systems.
• Proficiency with Python, SQL, and at least one data pipeline orchestration tool (e.g., Apache Airflow, Luigi, Prefect).
• Strong experience with cloud-based data platforms (e.g., AWS Redshift, GCP BigQuery, Snowflake, Databricks).
• Deep understanding of data modeling, data warehousing, and distributed systems.

Additional Skills

• Familiarity with DevOps practices (CI/CD, infrastructure as code, containerization with Docker/Kubernetes).
• Exposure to AI/ML-integrated solutions or interest in working alongside data science teams.
• Knowledge of data security and privacy regulations (e.g., GDPR, HIPAA).
• Familiarity with prompt engineering and how LLM-based systems interact with data.