Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com.
Job DescriptionLead, 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.
- 7+ years of experience in Data Engineering.
- Strong experience working with GitHub repositories and version control workflows.
- Hands-on experience developing and maintaining data pipelines in Databricks.
- Proven experience refactoring and maintaining legacy codebases.
- Strong understanding of data modeling and reusable component design.
- Experience building scalable data models for analytics and reporting use cases.
- Strong focus on data quality, performance, and reliability.
- Ability to work in cross-functional environments and contribute to continuous improvement.
- Ability to work independently and take ownership of initiatives after receiving high-level direction, driving tasks forward with minimal supervision.
- Experience using Genie (Databricks) (Plus).
All your information will be kept confidential according to EEO guidelines.
