We are looking for a hands-on Data Engineer with expert Python skills, deep database expertise
(Oracle, PostgreSQL, Couchbase), and cloud experience on Google Cloud Platform and AWS. You will modernize and
build scalable, high-performance data platforms, including AI-powered solutions using LLMs, RAG,
and agentic frameworks. You will work across the full engineering lifecycle: architecture, design,
coding, performance tuning, deployment, and production support.
What You'll Do
Python Engineering
• Build production-grade Python applications, backend services, and reusable components
using clean code, SOLID principles, and design patterns
• Develop ETL/ELT frameworks, ingestion services, validation utilities, and workflow
automation
• Build REST APIs and microservices with FastAPI, Flask, or similar
• Apply async, multithreading, and multiprocessing to optimize high-volume workloads
• Build database integration layers (connection pooling, bulk processing, transaction
management)
• Create tooling for database migration, schema comparison, data reconciliation, data quality,
and performance monitoring
• Own testing (unit, integration, mocking), CI/CD pipelines, code reviews, and production
troubleshooting
Database Architecture & Optimization
• Design scalable, secure relational and NoSQL data models, indexing, and partitioning
strategies
• Write and tune complex SQL, stored procedures, functions, and views, including execution
plan analysis
• Apply knowledge of database internals: concurrency, locking, isolation levels, storage
• Design replication, HA, backup, recovery, and DR solutions
• Lead modernization work, including Oracle-to-PostgreSQL and on-prem-to-cloud migrations
• Ensure data integrity, security, and governance
Cloud Engineering (Google Cloud Platform & AWS)
• Build and deploy cloud-native data solutions and serverless, event-driven Python
applications
• Work with services such as BigQuery, Cloud SQL, AlloyDB, Dataflow, Pub/Sub, Cloud Run,
Vertex AI, RDS, Aurora, S3, Redshift, Glue, Lambda, Step Functions, and Bedrock
• Automate infrastructure and deployments; implement monitoring and logging
• Apply cloud security best practices (IAM, encryption, secrets management, network controls)
• Optimize cloud performance and cost across hybrid and multi-cloud environments
Data Pipelines
• Design batch, near-real-time, and streaming pipelines using Python, SQL, and cloud-native
services
• Integrate data across Oracle, PostgreSQL, Couchbase, cloud databases, APIs, and object
storage
• Implement data quality checks, schema validation, audit logging, and automated recovery
• Work with Spark, Airflow, Kafka, and cloud orchestration tools
• Build observability and lineage to support analytics, operational, and AI/ML use cases
GenAI & Agentic AI
• Build and deploy production AI applications and agents using Python and LLMs
• Develop single- and multi-agent systems (planning, tool calling, multi-step orchestration) with
LangChain, LangGraph, CrewAI, AutoGen, or similar
• Design RAG architectures: ingestion, chunking, embeddings, vector indexing, hybrid
retrieval, reranking, and context optimization
• Work with OpenAI APIs, Vertex AI/Gemini, and AWS Bedrock
• Use vector databases such as pgvector, Pinecone, FAISS, or Chroma
• Build agents that securely interact with Oracle, PostgreSQL, Couchbase, and cloud data
platforms
• Develop reusable Python frameworks for LLM orchestration
Required Qualifications
• 8-12+ years in data engineering or software engineering
• Expert Python (OOP, decorators, generators, context managers, async) plus Pandas,
NumPy, SQLAlchemy, psycopg, python-oracledb, PyTest, boto3, and Google Cloud SDKs
• Deep hands-on experience with Oracle and PostgreSQL, including performance tuning
• Hands-on Google Cloud Platform and AWS data engineering experience
• Experience building and deploying LLM/RAG-based applications
Preferred Qualifications
• Couchbase experience
• Oracle-to-PostgreSQL migration experience
• Spark, Airflow, Kafka
• Multi-agent framework experience (LangGraph, CrewAI, AutoGen)
