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Lead, design, and scale data solutions to support Journey Analytics initiatives, with a strong focus on code quality, reusability, and reliable data platforms. This role is responsible for setting the technical direction, overseeing the evolution of data architectures, and leading a team of data engineers to deliver high-quality, performant datasets for analytics and reporting use cases.
The ideal candidate combines strong hands-on data engineering expertise with people leadership experience, and has a proven track record of driving scalable solutions in cross-functional environments.
Responsibilities
Lead and mentor a team of data engineers while actively contributing to development efforts and setting best practices in coding, architecture, and data engineering standards.
Define and drive the technical strategy for Journey Analytics data platforms, while remaining hands-on in the design and implementation of scalable solutions.
Actively contribute to the maintenance, optimization, and automation of code repositories in GitHub, ensuring high-quality and consistent development practices.
Participate directly in refactoring legacy codebases to improve maintainability, scalability, and reusability across multiple use cases.
Design and build modular, reusable data components to support multiple journeys and reduce duplication.
Develop and manage automated data pipelines in Databricks, ensuring reliability and scalability for downstream consumption.
Design and implement scalable data models to support current and future analytics use cases.
Ensure data quality, governance, performance, and reliability across all data pipelines and datasets through both oversight and direct contribution.
Collaborate closely with analytics, product, and engineering stakeholders to align data solutions with business needs and priorities.
Proactively identify risks, bottlenecks, and improvement opportunities, and take a hands-on role in driving mitigation strategies.
Promote continuous improvement of data processes, documentation, and engineering practices through both leadership and execution.
Lead hands-on delivery of Palantir Foundry data and AI solutions, building pipelines, ontology layers, and LLM-powered workflows while guiding engineers and advising client stakeholders.
As a Lead Data Engineer, you will design and implement AI-optimized data platforms, becoming a trusted advisor to clients while solving complex data engineering challenges.
Defines enterprise data-platform architecture and engineering standards, leads complex system design and technical decisions, and builds reliable data products while mentoring engineers across teams.
The ideal candidate will build and maintain scalable data solutions in Databricks, focusing on data governance, audience activation, and marketing analytics.
Own and advance data platform initiatives spanning lake architecture, batch and streaming pipelines, event governance, data quality, privacy, and AI-assisted engineering standards.
