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Salary: USD 100,000 - 140,000 / annual
Resilience is a category-defining platform integrating cybersecurity, cyber insurance, and cyber risk management. Founded in 2016 by experts from the US military and intelligence communities, the company combines risk quantification software, cybersecurity expertise, and A+ rated cyber insurance for mid-market and large organizations. Backed by leading VCs including General Catalyst, Lightspeed, and Founders Fund.
As a Data Engineer, you will take full ownership of building, scaling, and optimizing the data infrastructure that powers the platform, connecting cybersecurity visibility with cyber insurance analytics. You will sit at the heart of the engineering team, ensuring efficient and reliable data integration, storage, and processing across cross-functional teams.
In your first six months, you will:
• Design, develop, and maintain robust, scalable data pipelines for ingestion, transformation, and storage, ensuring data quality, reliability, and integrity throughout the pipeline lifecycle.
• Partner closely with data scientists, analysts, and application teams to design and implement scalable data models supporting business intelligence and analytics while integrating diverse internal and external sources.
• Address system bottlenecks, conduct capacity planning, and implement optimizations to elevate performance and reliability across cloud-based data systems.
• Establish and enforce data security policies, access controls, and compliance standards in alignment with regulatory measures.
• Provide technical mentorship to fellow engineers, fostering a culture of continuous learning while communicating technical concepts effectively to non-technical stakeholders.
The team operates with a bias to action, guided by core values of transparency, excellence, grit, and humility. Engineering team members are distributed across Baltimore, New York, San Francisco, and the eastern seaboard, operating loosely on Eastern Standard Time.
REQUIREMENTS:
• 3+ years of proven data engineering experience with hands-on expertise in data integration, data modeling, and ETL processes.
• Proficiency in SQL, Python, and JavaScript/TypeScript.
• Solid working knowledge of data warehousing concepts, cloud architectures, and modern data stack tooling (e.g., Dagster, AWS, Snowflake, dbt).
• Familiarity with data governance, security, and compliance best practices.
• Bachelor's degree in Computer Science, Data Science, or a related quantitative field (or equivalent practical experience).
• Strong analytical, problem-solving, and cross-functional communication skills, with a track record of managing multiple projects in a dynamic environment.
NICE-TO-HAVES:
• Master's degree in Computer Science or Data Science.
• Advanced certifications in data engineering or cloud platform domains.
