Live opening · Posted 11 days ago
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Description supplied by the original job listing.
We are building a next-gen eCommerce operations tool that provides real-time data, metrics, alerts, and the inherent capability for businesses to manage day-to-day operations. As a staff/senior/advanced engineer, you will design, build, and operate resilient microservices on Microsoft Azure using Java (Spring Boot, Spring WebFlux/Reactive), integrating rich data and streaming pipelines across SQL Server, Apache Kafka, and Azure Databricks.
You will act as a lead/architect on both transactional and analytical pipelines using Spark Structured Streaming (micro-batching) and Delta Lake, alongside event-driven microservices. Solutions will be secured via Azure Key Vault, monitored with Grafana, deployed on AKS, exposed through Azure API Management, and automated with GitHub Actions. You will collaborate with global product, platform, analytics, and security teams - acting as a technical anchor for best practices in performance, reliability, data engineering, and DevOps.
The candidate will have responsibilities across the following functions:
Design and Development:
Build scalable, reactive microservices using Java 17+, Spring Boot, and Spring Web Flux.
Design robust APIs using REST/JSON and OpenAI/Swagger, including pagination, versioning, concurrency controls, and idempotency.
An architect of distributed systems optimised for high availability, resiliency, and low latency.
Data and Messaging:
Relational Data: Model and optimise OLTP schemas for SQL Server, including indexing, query tuning, partitioning, and transactional workloads.
Event-Driven Systems: Develop Kafka producers/consumers, manage DLQs.
Big Data and Streaming:
Build and maintain Azure Databricks pipelines supporting both batch and streaming workloads.
Implement Spark Structured Streaming (micro-batching) pipelines for ingestion, enrichment, aggregation, and stateful stream processing.
Design and optimise Delta Lake architectures: ACID transactions, schema evolution, compaction (OPTIMISE), Z ordering, and efficient Lakehouse patterns.
Partner with analytics and ML teams on data modelling, feature engineering, and data quality frameworks.
Experience
9-12 yrs
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