You’ll own the backend systems that ingest high-volume fault data, serve results via APIs to thousands of concurrent users, and build data pipelines connecting upstream data sources to downstream consumers. This is a hands-on engineering role where you also contribute to exploratory data analysis and work closely with the data science team.
Requirements
Must-Have:
6+ years building production REST APIs with Java Spring Boot
Apache Flink or equivalent stream processing (Kafka Streams, Spark Streaming)
High-traffic system design — caching, connection pooling, rate limiting, horizontal scaling, circuit breakers
Data pipeline experience — batch/streaming ETL, scheduling, monitoring, error handling
SQL proficiency — complex queries, window functions, aggregations on large datasets (100M+ rows)
Azure Cloud — AKS, Azure Functions, Event Hubs, Service Bus, or equivalent services
Database design — PostgreSQL or similar RDBMS (indexing, query optimization, schema design)
Basic data analytics — comfortable running exploratory queries, spotting anomalies,
summarizing data distributions
Additional feedback from client:
“This specific position requires deep hands-on experience with high throughput IoT data stream processing and Azure-native services, and we’re looking for someone who can hit the ground running in those areas without significant ramp-up time.”
Job responsibilities
What You’ll Build:
High-Traffic REST APIs — Java Spring Boot services handling concurrent requests at scale
Streaming Data Ingestion — Huge volume Realtime IoT Data Ingestion pipelines
Data Pipelines — Batch/streaming ETL jobs transforming raw data into API-ready formats
Event-Driven Architecture — Webhooks and notifications for time-critical events
Exploratory Analytics — Query large datasets to validate data quality, identify patterns, and support the data science team
What we offer
Exciting Projects: Come take your place at the forefront of digital transformation With clients ac