Design, develop, and optimize scalable ETL/ELT pipelines for enterprise data ingestion, integration, and transformation.
Build and maintain data engineering workflows using Databricks, dbt, PySpark, SQL, and Python.
Develop Data Lake, Data Warehouse, and Lakehouse solutions, including data modeling and incremental processing.
Collaborate with business stakeholders to understand requirements, business processes, and challenges, and translate them into effective data and analytics solutions.
Proactively communicate with business users and technical teams to clarify requirements, provide project updates, address issues, and ensure solutions meet business expectations.
Ensure data quality, consistency, security, and governance through validation and automated testing.
Prepare and integrate enterprise data to support BI, AI/ML applications, and advanced analytics.
Develop AI/LLM solutions, including RAG, AI agents, API integrations, and intelligent workflow automation.
Monitor and optimize data pipeline performance, reliability, resource utilization, and costs.
Implement data orchestration, CI/CD, automated deployment, and monitoring practices.
Collaborate with BI, AI, IT, and business teams to support solution delivery, user testing, documentation, and continuous improvement.
2–4 years of hands-on experience in Data Engineering, ETL/ELT, and enterprise data platforms.
Strong SQL and Python skills, with practical experience in Databricks, PySpark, Delta Lake, and dbt.
Experience designing, developing, and optimizing production data pipelines, data models, and Lakehouse/Data Warehouse solutions.
Ability to understand business processes, gather and clarify requirements, and translate business needs into actionable technical solutions.
Strong verbal and written communication skills, including the ability to explain technical concepts to non-technical stakeholders.
Strong analytical, problem-solving, and interpersonal skills, with a proactive attitude and ability to collaborate across business and technical teams.
Familiarity with cloud data platforms (Azure/Microsoft Fabric preferred), Git, CI/CD, and data quality practices.
Understanding of AI/LLM technologies, API integrations, and AI-driven data processing or automation.
Nice-to-Have: Experience with RAG, AI agents, vector databases, Power BI, or enterprise ERP systems.
