Responsibilities

• Architect data and ML pipelines to support AI/ML and BI.
• Design cloud-native data architectures (AWS, GCP, Azure).
• Ensure the quality, governance, and scalability of data assets.
• Implement metadata, lineage, and governance frameworks.
• Develop feature stores, monitoring tools, and CI/CD pipelines.
• Collaborate with cross-functional teams to deliver data-driven insights.

Qualifications

• Proven experience in data engineering and architecture.
• Expertise in cloud platforms and ML integration.
• Strong communication skills and ability to work with cross-functional teams.

Key Skills

• Expertise in Spark, PySpark, SQL, Python
• Experience with orchestration tools (Airflow, dbt)
• Knowledge of data warehousing, lakehouse architectures
• Understanding of ML lifecycle integration
• Experience with real-time data, vector, and graph databases
• DevOps tools: Docker, Kubernetes, Terraform

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