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Job Description
KEY RESPONSIBILITIES AND DUTIES
Lead the architecture, design, and evolution of a scalable Data & AI platform using Databricks lakehouse architecture.
Integrate enterprise systems including SAP Datasphere, SAP S/4HANA, and Denodo into a governed, self-service data ecosystem.
Design, develop, and maintain complex data pipelines, feature stores, and API-driven integrations.
Build and implement production-grade MLOps frameworks, including model versioning, CI/CD automation, deployment, and monitoring.
Establish and enforce data engineering standards for ingestion, transformation, quality, and governance-as-code practices.
Develop automated and scalable solutions to support enterprise analytics, AI, and digital initiatives.
Provide technical leadership through code reviews, mentoring, and coaching of junior and mid-level engineers.
Collaborate with architects, product owners, governance teams, and business stakeholders to align technical solutions with enterprise data strategy.
Drive adoption of best practices in platform engineering, security, automation, and operational excellence.
Support API-first and event-driven architectures, ensuring secure service-to-service communication and role-based access controls (RBAC). "
MUST HAVE
"
SPECIFIC KNOWLEDGE AND SKILLS
Bachelor''s degree in Computer Science, Data Engineering, Information Technology, or a related technical field (or equivalent experience).
4-6 years of hands-on experience in data engineering, software engineering, data platform development, or data architecture.
Advanced proficiency in Python and SQL.
Strong experience with Apache Spark and distributed data processing frameworks.
Hands-on expertise with modern Lakehouse architectures, preferably Databricks.
Proven experience designing and implementing MLOps pipelines and machine learning deployment frameworks.
Experience building and managing feature stores, semantic layers, or internal AI/data platform services.
Strong knowledge of API-first architectures and enterprise integration patterns.
Experience with CI/CD pipelines, automation, source control, and deployment processes.
Knowledge of security best practices, including RBAC, authentication, authorization, and secure service-to-service communication.
ADDITIONAL SKILLS AND OTHER REQUIREMENTS
Experience with SAP Datasphere and
SAP S/4HANA
integrations.
Knowledge of Denodo data virtualization platforms.
Experience implementing data governance, metadata management, and governance-as-code frameworks.
Familiarity with cloud-native data and AI services.
Ability to design self-service analytics and data consumption solutions. "We need "LOCALS" and currently accepting "W2" profiles only"
