What You'll Do
- Data Platform Development: Contribute to the design and implementation of scalable, reliable data pipelines, storage solutions, and efficient access layers using modern cloud-native technologies.
- Pipeline Execution: Build, optimize, and maintain core data platform components, including event streaming infrastructure, lakehouse storage layers, and both batch and streaming ETL workflows.
- Data Quality & Security: Implement best practices for data quality, data lineage, and access controls to ensure robust security, regulatory compliance, and overall trust in our data assets.
- Cross-functional Collaboration: Work closely with Product, Analytics, Infrastructure, and Security teams to deliver data capabilities that support team-specific and organizational goals.
- Team Mentorship: Provide technical guidance and code reviews for other engineers on the team, helping to champion clean code, consistent engineering standards, and platform stability.
- Performance & Observability: Evaluate, test, and integrate new tools and technologies that actively improve data pipeline performance, systems observability, and overall cost efficiency.
What You'll Bring
- 7+ years of software engineering experience with a strong background in data infrastructure, distributed systems, or backend data platform engineering.
- Proven experience building and maintaining production-grade data pipelines at scale (e.g., handling large data volumes, optimizing job execution, and ensuring data reliability).
- Strong hands-on experience with modern data ecosystem tools, such as Snowflake, dbt, Kafka, Airflow, Spark, and cloud-native services (e.g., AWS, GCP, or Azure).
- Experience building and integrating APIs and services for data ingestion, access, and pipeline observability.
- Good understanding of secure data handling, including familiarity with enterprise-grade security practices and data privacy considerations (such as SOC2 or GDPR).
- Strong communication and collaboration skills, with a track record of partnering successfully across engineering and analytics teams.
- Experience working within a startup or high-growth SaaS environment is highly preferred.
- Exposure to or interest in AI/ML data pipelines or real-time analytics architectures.
Here’s Why You Should Apply
- What is engineering working on? Our FQ Engineering Blog showcases a number of our recent efforts straight from the engineers working on them. Check it out!