Desired Skills and Experience
● Strong background in building robust Python-based systems and SQL knowledge
● Architect QE solutions on cloud platforms such as Azure (ADLS, Databricks, ADF, Synapse), AWS (Glue, Lambda, Aurora, Redshift), GCP (Big Query, GKE), Snowflake or equivalent.
● Familiarity with containerization, orchestration, and scalable architecture
● Knowledge of natural language processing (NLP) is a must.
● Hands-on experience with multiple LLMs like OpenAI, Claude, Gemini, Llama, etc. along with implementation of AI Agents and Model Context Protocols (MCP).
● Knowledge of using hyperscaler products like Hadoop, Pyspark, Databricks, Snowflake and AWS EMR
● Strong Experience in building multiple frameworks using Streamlit, Pytest, Playwright, Selenium, Seaborn, Plotly, Langchain, LangGraph, Appium, Robot and related Libraries.
● Strong knowledge of CI/CD pipeline (Airflow/Azure DevOps/Jenkins/GitHub Actions)
● Ability to independently design, develop and architect highly available, highly scalable accelerators and frameworks from scratch
● Proficiency in data manipulation libraries like pandas or NumPy for Python and experience with data visualization tools to build analytical models and statistics as required.
● Good knowledge of RESTful interfaces / Microservices concepts
● Good to have experience with GenAI, LLMs, RAG pipelines, and vector databases (Pinecone, FAISS)
● Define and implement data quality engineering frameworks for data lakes, data warehouses, and analytics platforms.
● Architect and implement data test automation using Python, SQL, and data testing frameworks.
● Define data quality KPIs, dashboards, and metrics.
● Identify risks related to data accuracy, latency, and compliance and communicate them proactively.
● Partner with data engineers, product owners, and architects to drive quality from design to delivery.
● Mentor QE engineers and SMEs.
● Review test strategies, frameworks, and implementations.
● Support proposal discussions, estimations, and solutioning from QE perspective
● Understanding of the concepts involved in Functional & Non-Functional validations
● Knowledge of Dockers and Kubernetes or containers is good to have
● Ability to quickly learn and apply complex technical information to automation solutions.
● Attention to detail and ability to effectively escalate issues and manage escalations.
● Experience with Agile methodology
● Ability to handle multiple assignments at one time and meet delivery dates.
● Project Management Tools like ADOTestPlans/ALM/Rally/JIRA knowledge is a plus.
● Additional programming languages like Java, JavaScript, Rust or R knowledge is a plus.
● Excellent written and communication skills.