Job description
About Reach Security:
Reach Security (https://reach.security) builds self-driving cybersecurity. Reach employs the first generative AI for cybersecurity to oversee an organization’s current security stack, aiming to achieve the best possible security posture with the products already in use.
About the Role:
We are seeking Data Engineers at all levels to design, build, and manage robust data pipelines to support analytics use cases within a Datalake or Lakehouse architecture. You will play a critical role in developing scalable solutions using Apache Airflow or Dagster to ingest, transform, and manage large volumes of data efficiently and reliably for analytic workloads.
The ideal candidate is a motivated problem solver who prioritizes high-quality solutions and excels in navigating ambiguity. As an early team member, you will have the opportunity to take ownership of various aspects of our backend from day one. Your role will be pivotal in establishing engineering best practices, balancing engineering priorities with business needs, and identifying innovative approaches to deliver outstanding value to our users. Your engineering knowledge will be applied by designing top-notch architectures, offering insightful feedback on technical designs, solving difficult problems and conducting thorough code reviews, all aimed at ensuring the software we build is both maintainable and dependable.
In this role, you will:
Design, implement, and maintain scalable and reliable data pipelines using Apache Airflow or Dagster.
Work closely with Platform and Product teams to ensure efficient data ingestion, transformation, and storage strategies.
Develop and optimize data models and schemas that power analytical queries and reporting.
Ensure data integrity, quality, and consistency across Data Warehouse, Data Lake, and Lakehouse environments.
Troubleshoot and optimize performance bottlenecks in complex data processing workflows.
Collaborate in defining engineering best practices, standards, and processes to enhance team productivity and quality.
Proactively identify opportunities to enhance pipeline efficiency, scalability, and reliability.
Success in this role requires:
3+ years of experience in data engineering with a specific focus on building and managing data pipelines.
Strong proficiency in Python and experience with Apache Airflow or Dagster.
Expertise in developing solutions within Data Warehouse, Data Lake, and Lakehouse architectures.
Deep understanding of ETL/ELT processes, data transformation techniques, and workflow orchestration.
Experience working with cloud-based data platforms and services (AWS, Azure, GCP, etc.).
Solid foundation in data modeling, schema design, and optimization techniques.
Excellent problem-solving skills, capable of addressing challenges around data consistency, performance, and scalability.
Strong communication skills with the ability to articulate complex data engineering concepts clearly.
A proactive and collaborative mindset, comfortable working independently and within fast-paced teams.
Must be a US citizen or Green Card holder.
Ways to stand out:
Experience with both batch and streaming data pipelines.
Demonstrated expertise in advanced database schema design, query optimization, and database scaling.
Familiarity with Infrastructure as Code (IaC) tools such as Terraform, Pulumi, or AWS CDK.
Proven ability to align data engineering solutions closely with strategic business objectives.
Work arrangement:
Competitive salary and equity.
Comprehensive health, dental, and vision insurance.
Remote work flexibility.
