Source description
About the role
This role is responsible for defining and leading the engineering approach for complex, data-driven and AI-enabled features that deliver measurable business outcomes through customer insights. As a senior member of the team, you will serve as a technical thought leader and hands-on contributor, partnering with internal teams to design and deliver scalable analytics, AI, and agentic intelligence solutions.
Responsibilities
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Lead the engineering design and implementation of enterprise-scale data, analytics, and AI platforms
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Design and deliver end-to-end solutions from architecture through implementation and production support
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Apply deep expertise in big data and real-time processing technologies, including Hadoop, Spark, Kafka, and Spark Streaming
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Engineer data pipelines and feature stores that reliably power machine learning models and real-time scoring
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Define and implement data and AI engineering best practices, ensuring solutions are scalable, resilient, and secure
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Provide technical thought leadership across modern data, AI, and analytics platforms
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Collaborate effectively with onshore and offshore engineering, data science, and product teams
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Function as a senior individual contributor while also leading teams or workstreams at the program level
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Act as a trusted technical advisor, translating business needs into data-driven and AI-powered solutions
Requirements
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Proven experience delivering complex, enterprise-scale data and analytics platforms
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Strong hands-on expertise with distributed data processing and streaming architectures (Hadoop, Spark, Kafka, Spark Streaming)
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Experience supporting machine learning and AI workloads, such as feature engineering, model input pipelines, and real-time inference
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Experience with NoSQL and low-latency data stores (e.g., Redis) supporting real-time analytics and AI inference
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Demonstrated ability to lead solution design and delivery across multiple teams
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Excellent collaboration and communication skills, with experience working across geographically distributed teams
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Proficiency in Data Integration and Data Security within real-time and Big Data ecosystems, including knowledge of Kerberos
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Desired skills:
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Familiarity with agentic or autonomous processing patterns (e.g., event-driven workflows, rules-plus-AI decisioning) is highly desirable
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Experience with additional real-time streaming technologies like Flink or Storm
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Familiarity with Cloud Technologies such as Azure, AWS, or GCP
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Working knowledge of machine learning algorithms, statistical analysis, and programming languages (Python or R)
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Strong command of Visual Analytics Tools, with a focus on Tableau
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