Back
Tier-1 brand, mid-level generalist role, and broad multi-tool requirements increase competition.
Core data engineering skills are transferable, but specialized platform and AI-agent experience raise domain specificity.
Multiple mandatory technologies and an explicit 5+ years requirement increase filtering strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own the design and development of scalable data-intensive platforms using Data Virtualization and Data Domain methodologies.
Optimize and scale data processing and analytics solutions leveraging technologies like Spark, Starburst, Snowflake, and real-time processing tools.
Integrate AI agents and applied AI technologies (e.g., MCP servers, LLMs, RAG techniques) into data engineering workflows for automation and intelligent data processing.
5+ years experience in Big Data or large-scale enterprise application development using tools like Databricks, Scala, Java, and Python ecosystem.
Bachelor’s degree or equivalent experience required.
Strong expertise in data processing (Spark, Starburst, Snowflake), data storage (Hadoop, MongoDB, Oracle), ETL tools (Airflow, Ab Initio, Talend), and SQL/PL-SQL development with database tuning.
Experience with AI agents design and integration, MCP servers configuration, and familiarity with Large Language Models and Retrieval-Augmented Generation technologies.
Experienced in building and managing large scale distributed data systems and data mesh architectures in enterprise environments.
Capable of leveraging advanced AI and data virtualization technologies to innovate data engineering solutions.
Strong technical foundation with practical skills in integrating AI-powered agents and applying LLMs for enhancing data workflows and automation.