Data Engineer

• Design, implement, and optimize data pipelines, platforms, and architecture to support analytics, AI/ML, and business intelligence across the company
• Architect E2E data solutions in cloud environments (Azure or AWS), encompassing data ingestion, data lake, data warehouse, real-time streaming, modeling, and consumption
• Develop and maintain enterprise data architecture frameworks, standards & principles
• Lead the design and implementation of scalable, secure, and high-performance data pipelines
• Define and enforce data engineering standards, best practices, and governance across teams
• Build and maintain robust ETL/ELT pipelines for structured and unstructured data sources
• Optimize data workflows for performance, reliability, and cost-efficiency
• Ensure data quality, lineage, and observability across all pipelines
• Evaluate and implement modern & emerging tools and cloud platforms (e.g., Azure, Databricks) to improve data engineering capabilities
• Drive automation and CI/CD practice in data engineering workflows
• Integrate data platforms with enterprise systems including ERP, CRM, and external data sources
• Lead and mentor a team of data engineers, fostering a culture of innovation and continuous improvement
• Partner with IT and business leaders to align data engineering initiatives with strategic goals
• Promote agile methodologies and cross-functional collaboration
• Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA) and internal security policies
• Implement data governance frameworks in collaboration with data stewards and legal teams.

Enterprise Data Engineering Lead

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Data Engineer