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About the role
Required Qualifications: 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Technical Experience (4+ years) Hands-on engineering experience using: Python, Spark, Iceberg, Hive Dremio or virtualization/federation/semantic layer tools Any major cloud Azure or GCP ETL tools: Ab Initio (preferred), Informatica, DataStage Databases: Oracle, MS SQL, Teradata Orchestration: Autosys, Airflow (preferred) Strong SQL development and tuning skills Proven experience supporting and optimizing large enterprise-scale data environments. Working with modern Data Warehousing, Data Lakes, and Lakehouse architectures. Monitor, tune, and troubleshoot large-scale distributed systems. Diagnose memory, performance, partitioning, and query-related inefficiencies across compute engines. Ensure system reliability, scalability, and high throughput in enterprise data environments. Expertise in designing / implementing modern data platforms with: Standardization of patterns, frameworks, and reusable components Automated code-generation, CI/CD and DevOps best practices Cloud-native and open-table formats (e.g., Iceberg) Hands-on experience in GenAI, Agentic AI, and LLM adoption, such as: Building RAG architectures, vector stores, embedding pipelines Integrating data systems with enterprise AI platforms Using LLMs to improve data quality, metadata extraction, or automation of data engineering tasks Familiarity with data governance and security frameworks, including privacy, compliance, lineage, and access control. Strong communication skills and collaborate with cross-technical / functional teams. Ability to work in an agile, fast-paced environment while delivering high-quality results. Passion for innovation, continuous learning, and adopting modern practices including AI-driven development. .
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