Lead Data Platform Engineer/Architect

About Niche

Niche is the leader in school search. Our mission is to make researching and enrolling in schools easy, transparent, and free. With in-depth profiles on every school and college in America, 140 million reviews and ratings, and powerful search tools, we help millions of people find the right school for them. We also help thousands of schools recruit more best-fit students, by highlighting what makes them great and making it easier to visit and apply.

Niche is all about finding where you belong, and that mission inspires how we operate every day. We want Niche to be a place where people truly enjoy working and can thrive professionally.

 

About The Role

We are looking for a Lead Data Platform Engineer / Architect who would provide their deep expertise and technical leadership as we build the next generation of Niche’s data platform. You’ll lead the architecture and development of a scalable data platform and capabilities that can handle the volume and complexity of data while ensuring data accuracy, availability, observability, security, and optimum performance. You’ll be developing and maintaining our centralized, governed, and certified data models and source of truths (DW) for consistent reporting internally and externally. You’ll also be instrumental in building out a scalable data sharing architecture for our partners to power our data licensing product. The ideal candidate will be a technical expert with solid experience and wins under their belt in building, improving and supporting large scale data platforms. This role will report to the Head of Data Engineering.

 

What You Will Do

During the First Month:

•  Learn about Niche by meeting with various team members to learn more about our company through our Onboarding meetings
•  Build strong relationships with data engineering  team members, understand the day to day operating model, and stakeholders that we interact with on a daily basis.
•  Begin discussions to understand our data platform vision, and key challenges. Develop early ideas towards the architecture of our platform.

Within 3 Months:

•  Complete a detailed assessment of the current state of our data platform that covers tech stack, data architecture, security, quality, and design of pipelines.
•  Deliver first versions of the data platform engineering  best practices, and standards documentation for the data engineering team; ensure in line with wider engineering standards.
•  Identify areas for improving data platform engineering processes and streamline data pipelines, making them more efficient.
•  Incinitial data quality tooling and framework to improve data accuracy, consistency, and completeness.

Within 6 Months:

• Lead the development of data platform, building out data pipelines, and data warehouse layers. Ensure the platform can handle the scale and complexity of data efficiently.
• Enforce data engineering practices, incorporating data modeling into our workflow, standardized ETL/ELT processes, architecture and design patterns, and core data warehousing principles.
• Stay updated on emerging data technologies and start implementing innovative solutions to address business needs.

Within 12 Months:

• Contributions have led to significant progress in implementing the long-term data platform strategy and increasing the quality and accessibility of data.
• We have strengthened data governance practices, ensuring that data is governed, certified, and available as a source of truth for reporting.

 

What We Are Looking For

• Bachelor’s degree in Computer Science, Data Science, Information Systems,  a related field, or equivalent experience.
• 10+ years of experience in data engineering, software engineering, or a related field, with a minimum of 3 years as a senior level engineer with data platform architecture experience.
• Demonstrated experience of building, optimizing, and supporting large scale data platforms.
• Software engineering mindset, leading with the principles of source control, infrastructure as code, testing, modularity, automation, CI/CD, and observability.
• Strong knowledge and understanding of the modern data platform, and its key components - ingestion, transformation, curation, quality, governance, and delivery.
• Experience of working with a wide variety of source systems, building data pipelines to ingest both streaming and batch data, and delivering clean data for downstream usage in reporting, analytics, data science, and applications.
• Knowledge of data modeling techniques (3NF, Dimensional, Vault), data lake, data warehouse, data mart, design patterns (lambda, kappa, medallion,