Teams at DATAECONOMY

Build scalable ETL/ELT pipelines in Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, Workflows, and Unity Catalog. Ingest batch and streaming data across medallion architecture layers, develop transformations and data quality rules, and implement orchestration, monitoring, alerting, and automation. The role requires cloud data engineering, data modeling, performance tuning, CI/CD, and strong knowledge of lakehouse architecture, governance, and distributed systems.

Designs and builds secure, production-grade Generative AI and agentic AI applications using Python, LLMs, cloud services, and AI frameworks. Responsibilities include developing scalable AI pipelines, integrating Databricks data with AWS services, applying RAG and vector databases, evaluating models, implementing guardrails, researching emerging tools, improving application infrastructure, and guiding team members.

Build scalable production-grade ETL/ELT pipelines on Databricks using PySpark, Spark SQL, Delta Lake, Delta Live Tables, and Workflows. Develop data ingestion, transformations, quality rules, orchestration, monitoring, and automation across Bronze, Silver, and Gold layers. Apply dimensional data modeling, streaming, cloud platforms, CI/CD, governance, security, performance tuning, and lakehouse architecture. Explain technical concepts and architecture decisions using real project examples.