Senior Data Engineer

Tasks

• Develop production-grade Python applications, APIs and reusable frameworks
• Design, build and optimize scalable data pipelines (Batch, Real-Time & Streaming)
• Perform advanced SQL development and database performance tuning
• Lead Oracle-to-PostgreSQL and on-premises-to-cloud migrations
• Build and deploy cloud-native data solutions across GCP and AWS
• Implement ETL/ELT frameworks, data validation and data quality solutions
• Develop GenAI applications using LLMs, RAG and Agentic AI frameworks
• Build single-agent and multi-agent systems with tool calling and orchestration
• Implement secure, scalable and high-performance database integrations
• Own architecture, development, testing, deployment and production support

Requirements

• 8–12+ Years
• STRONG HANDS-ON EXPERIENCE WITH PYTHON, ORACLE, POSTGRESQL, GCP & AWS
• Advanced Python – OOP, Async, Multithreading & Multiprocessing
• SQL – Complex Queries, Stored Procedures & Performance Tuning
• Oracle & PostgreSQL – Database Architecture & Optimization
• Couchbase – NoSQL Database
• GCP – BigQuery, Cloud SQL, AlloyDB, Dataflow, Pub/Sub & Vertex AI
• AWS – RDS, Aurora, S3, Redshift, Glue, Lambda & Bedrock
• REST APIs & Microservices – FastAPI / Flask
• ETL/ELT, Data Pipelines & Data Integration
• GenAI, LLMs, RAG & Agentic AI
• LangChain, LangGraph, CrewAI & AutoGen
• Vector Databases – pgvector, Pinecone, FAISS & Chroma
• PyTest, CI/CD, Cloud Security & Monitoring
• Nice-to-have: Couchbase hands-on experience
• Nice-to-have: Oracle-to-PostgreSQL migration experience
• Nice-to-have: Apache Spark, Airflow & Kafka
• Nice-to-have: LangGraph, CrewAI & AutoGen
• Nice-to-have: OpenAI APIs, Vertex AI / Gemini & AWS Bedrock
• Nice-to-have: Experience with financial services or enterprise-scale data platforms

Schedule

• Hybrid – 3 Days Onsite
• W2 Only – No C2C
• Interview: Virtual

Location

• Phoenix, AZ