Solution Architect – Cloud Solutions

Role Summary:
We are seeking a Solution Architect with expertise in Python and Java-based services, and advanced database/SQL and data pipeline performance optimization. This role will architect, design, and scale data-intensive, cloud-native platforms supporting analytics, operational systems, and AI/ML workloads. The ideal candidate combines deep system design skills, database knowledge, DevOps, and the ability to build end to end enterprise solutions in cloud environments.
Key Responsibilities
Cloud Architecture & Platform Design
Own end-to-end cloud solution architecture for data-driven platforms
Design scalable, fault-tolerant, cloud-native systems using Python and Java
Define architectural standards for microservices, data access layers, and APIs
Ensure solutions meet performance, security, cost, and compliance requirements
Continuously monitor and incrementally evolve system performance, reliability
Python Solution
Architect and deploy Python services in “tandem” with ML Engineers:
Data ingestion, transformation, and orchestration
Analytics, reporting, and decisioning systems
ML/AI pipelines and feature processing
Python frameworks and tools such as:
FastAPI / Flask
Pandas, PySpark, Dagster
Java-Based Data Services
Design and govern Java backend and data services, including:
REST / gRPC APIs
High-throughput data access services
Streaming and event-driven services
Work with technologies such as:
Spring Boot / Spring Cloud
Connection pooling, caching, and resilience patterns

Database Architecture & SQL Optimization

Design and optimize database architectures for both OLTP and OLAP workloads
Deeply analyze and tune SQL queries:
Query plans, indexes, partitions, joins, and materialized views
Locking, concurrency, and transaction isolation
Optimize performance across:
Relational databases (Postgres, MySQL, SQL Server, Oracle)
Cloud-native databases (Aurora, Cloud SQL, Azure SQL)
Data warehouses (Snowflake, BigQuery, Synapse)
Lead efforts in:
Schema design and normalization/denormalization tradeoffs
Indexing strategies and partitioning
Query refactoring and workload tuning
Database cost and performance optimization
Data & Streaming Platforms
Architect solutions using:
Data lakes and lakehouses (S3/ADLS/GCS, Delta, Iceberg, Hudi)
Streaming platforms (Kafka, Flink, Spark Structured Streaming)