Staff Data Engineer
Namely’s mission is to help mid-sized companies build a better workplace. We’re an HR, payroll, and benefits platform that provides the technology, data, and support that HR professionals need and employees love to use. People are at the center of everything we do, and we believe every company and employee deserves a great workplace, supported by innovative HR technology. At Namely, we are problem solvers, self-starters, and obsessed with creating the best experience for our clients.
Do you have a passion for building data architectures that enable smooth and seamless product experiences? Are you an all-around data enthusiast with a knack for ETL? At Namely, we're hiring a Staff Data Engineer to help build and optimize the foundational architecture of our product's data.
We’ve built a strong data engineering team to date, but have a lot of work ahead of us, including:
- Migrating from relational databases to a streaming and big data architecture, including a complete overhaul of our data feeds
- Defining streaming event data feeds required for real-time analytics and reporting
- Leveling up our platform, including enhancing our automation, test coverage, observability, alerting, and performance
As a Staff Data Engineer, you will work with the development team to construct a data streaming platform and data warehouse that serves as the data foundations for the Namely product.
Help us scale our business to meet the needs of our growing customer base and develop new products on the Namely platform. You'll be a critical part of our growing company, working on a cross-functional team to implement best practices in technology, architecture, and process. You’ll have the chance to work in an open and collaborative environment, receive hands-on mentorship and have ample opportunities to grow and accelerate your career!
- Build our next generation data warehouse
- Build our event stream platform
- Translate user requirements for reporting and analysis into actionable deliverables
- Enhance automation, operation, and expansion of real-time and batch data environment
- Manage numerous projects in an ever-changing work environment
- Extract, transform, and load complex data into the data warehouse using cutting-edge technologies
- Build processes for topnotch security, performance, reliability, and accuracy
- Provide mentorship to fellow team members
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Operations Research, or related field required
- 10+ years of experience building data pipelines
- 4+ years of experience building data frameworks for unit testing, data lineage tracking, and automation
- Fluency in Scala and at least one other server-side programming languages (e.g. Python, Java, Go)
- Proficient on Apache Spark
- Familiarity with streaming technologies (e.g., Kafka, Kinesis, Flink)
Nice to Have
- Familiarity with Machine Learning
- Familiarity with Looker a plus
Namely was founded in 2012 to create an HR platform as intuitive as social media, but powerful enough to support the complexity of today’s workforce. Our belief is that great companies are built on a great employee experience, which is why we created the first HR platform employees love to use. In fact, unlike most traditional HR software, 78% of our clients’ employees log in to Namely at least once per month! Namely is backed by some amazing VCs including Sequoia, and serves companies in just about every industry and state nationwide. We love mid-sized companies because they’re mission-driven, client-obsessed, and care deeply about their employees... just like us. We believe in giving you the tools you need to do the best work of your career, and we’re just getting started.
We invite you to fill out the EEO survey below as part of our ongoing diversity initiatives at Namely. Your participation in the survey is completely optional and voluntary, and none of the information you provide will be considered in the hiring process or with respect to any employment decision made by Namely. Namely will have access only to anonymized data submitted through these surveys.