About the Role
The role involves building and maintaining core components of the data platform that power analytics, reporting, and decision-making across the organization. The engineer will work on improving data reliability, scalability, and access while collaborating with multiple teams to deliver high-quality data solutions.
Responsibilities
- Design and implement scalable data pipelines for large-scale data processing
- Collaborate with analytics and product teams to understand data requirements
- Optimize data storage and retrieval for performance and cost efficiency
- Ensure data accuracy and consistency across systems
- Develop and maintain ETL workflows and data transformation logic
- Monitor pipeline health and troubleshoot data quality issues
- Work with distributed systems and cloud-based data platforms
- Improve data observability and monitoring practices
- Support the evolution of the data warehouse architecture
- Integrate new data sources into existing infrastructure
- Evaluate and adopt new data technologies and frameworks
- Write clean, maintainable, and well-documented code
- Participate in code reviews and system design discussions
- Contribute to data governance and security standards
- Assist in capacity planning for data growth
- Collaborate on incident response related to data systems
- Drive automation of operational data tasks
- Help define best practices for data modeling
- Support reproducibility and versioning of data pipelines
- Work closely with data scientists and analysts
- Ensure compliance with data privacy regulations
- Contribute to architectural decisions for long-term scalability
- Mentor junior engineers on data engineering principles
- Participate in on-call rotations for critical data services
- Evaluate performance bottlenecks in query execution
Nice to Have
- Master’s degree in Computer Science or related field
- Experience with real-time data processing systems
- Background in financial or compensation data systems
- Contributions to open-source data projects
- Experience with data mesh or domain-driven data architectures
- Knowledge of machine learning data pipelines
- Familiarity with data cataloging tools
- Experience in SaaS environments
- Prior work in people analytics or HR technology
- Exposure to regulatory compliance in data handling
Compensation
$180,000 - $240,000 per year
Work Arrangement
Hybrid
Team
Data Engineering
Our Data Stack
- We use Google Cloud Platform as our primary cloud infrastructure
- Data is processed using Apache Airflow and managed through BigQuery
- Kafka is used for real-time data streaming and event ingestion
- We rely on dbt for transformation workflows and data modeling
- Data observability is handled through custom tooling and monitoring systems
Impact You’ll Make
- Own the end-to-end design and delivery of critical data pipelines
- Enable faster and more accurate reporting through improved data quality
- Drive adoption of scalable data practices across engineering teams
- Help shape the future roadmap of the data platform
- Deliver infrastructure that supports rapid product iteration
Yes
