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)