Data Engineering Lead

About UsAt Resilience, we’re creating a new category that integrates cybersecurity, cyber insurance, and cyber risk management.
Founded in 2016 by experts from across the highest tiers of the US military and intelligence communities – and built by prominent leaders and innovators from the insurance, technology, and cybersecurity industries – Resilience is rewriting the rules of how cyber risk is assessed, measured, and managed. Our integrated cyber risk solutions connect risk quantification software, cybersecurity experts, and A+ rated cyber insurance, all purpose-built for middle and large organizations. 
Guided strongly by our mission and four core values - transparency, excellence, grit, and humility, our culture uniquely blends many different backgrounds, experiences, and skills from across industries and geographies - all focused on helping our clients and partners stay ahead of the bad guys. We are a cybersecurity company, a Cyber and Tech E&O-focused MGA, a fintech startup, and a data science powerhouse, all purposefully built into one. 
Resilience is proud to be backed by leading technology investment firms, including General Catalyst, Lightspeed Venture Partners, Intact Ventures, Founders Fund, CRV, and Shield Capital. With headquarters in San Francisco, Resilience’s team is globally dispersed, with offices in New York, Chicago, Baltimore, Los Angeles, Toronto, and London. Resilience offers insurance coverage through its licensed and appointed insurance agents and security services through its expert security team.  
As the Data Engineer, Lead at Resilience, you will be responsible for the development and maintenance of our data pipelines, data infrastructure, and systems.You will collaborate closely with cross-functional teams, including data scientists, analysts, and software engineers, to ensure efficient and reliable data integration, storage, and processing. This is a critical role that requires technical expertise, a strong understanding of data engineering practices, and a strategic mindset.
This position is full-time and remote-friendly. Our engineering team currently operates loosely on Eastern Standard Time, with employees located in Baltimore, New York, San Francisco, and scattered about the eastern seaboard.

Responsibilities:

• Technical Leadership: Act as a subject-matter expert for data engineering practices, providing technical guidance and hands-on support to peers and junior engineers. Champion best practices for scalable, efficient, and secure data systems.
• Data Infrastructure Strategy: Contribute to the strategic design and implementation of our data infrastructure to support current and future organizational needs. Research and recommend new tools, technologies, and methodologies to optimize processes.
• Data Pipeline Development: Design, develop, and maintain robust, scalable data pipelines for data ingestion, transformation, and storage. Ensure the quality, reliability, and integrity of data throughout the pipeline lifecycle.
• Data Modeling and Integration: Collaborate with data scientists, analysts, and application teams to design and implement data models that support analytics, reporting, and business intelligence needs. Integrate data from diverse internal and external sources for seamless accessibility.
• Performance Optimization: Analyze and address bottlenecks, inefficiencies, and processing issues in data systems. Conduct capacity planning and implement optimizations to improve performance and reliability.
• Data Security and Governance: Work with stakeholders to establish and enforce data security and governance policies. Implement appropriate access controls, data privacy measures, and compliance with regulatory standards.
• Cross-Functional Collaboration: Partner with teams across the organization to align data engineering efforts with business objectives. Clearly communicate technical concepts to stakeholders with varying technical backgrounds.
• Mentorship: Provide mentorship and technical support to other engineers on the team, fostering a culture of continuous learning and technical excellence.

Qualifications:

• Bachelor’s or Master’s degree in computer science, data science, or a related field. Advanced certifications in data engineering or relevant domains are a plus.
• Proven experience (5+ years) in data engineering, with hands-on expertise in data integration, data modeling, and ETL processes.
• Proficiency in programming languages such as Python, SQL, and Javascript/Typescript.
• Strong understanding of data warehousing concepts, data architecture, and cloud-based platforms (e.g., Dagster, AWS, Snowflake, dbt).
• Experience with data governance, security, and comp