Data Scientist (IFE DCC)

Responsibilities

• Design, develop, and maintain efficient ETL pipelines for ingesting, processing, and transforming large-scale data from multiple sources (batch and near real-time).
• Design and implement scalable data architectures and analytics pipelines , leveraging modern data platforms such as Databricks and Spark-based ecosystems.
• Design and implement efficient data models (dimensional, normalized, and curated layers) to support analytics and operational use cases.
• Define and enforce data quality, validation, and observability frameworks
• Drive performance tuning and cost optimization across compute and storage layers.
• Collaborate with engineering, platform, and product teams to operationalize data-driven insights within production environments.
• Drive the exploration and adoption of AI/ML use cases, defining architecture, selecting best-fit tools and frameworks, and enabling scalable, production-grade data-driven intelligence across platforms.

Requirements

• Strong background in analyzing large and complex datasets using distributed data processing frameworks such as Spark, Databricks, or similar platforms .
• Experience designing and implementing data architectures, ETL/ELT pipelines , and scalable data processing solutions .
• Experience working with Databricks ecosystem (Delta Lake, Spark SQL, Databricks workflows) is highly desirable.
• Proficiency in SQL and Python for data processing and transformation.
• Demonstrated ability to solve multidisciplinary, data-driven problems.
• Strong understanding of data modeling, data warehousing concepts, and lakehouse architecture.
• Experience with cloud platforms (AWS, Azure).
• Experience with Kubernetes (K8s), containerized workloads, and microservices infrastructure is a plus.
• Experience with designing and enabling secure, scalable data sharing using open standards (e.g., Delta Sharing) to support cross-organization data access is a plus
• Experience building or supporting data pipelines for AI/ML use cases, including feature engineering, data preparation, and integration with tools such as MLflow, LLM frameworks, or vector databases is a plus.
• Relevant certifications such as Databricks Certified Data Engineer Associate/Professional and Python Institute certifications (e.g., PCEP, PCAP) are a plus.
• Ability to work independently and as part of a team.

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Company Intel

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