Strategic Legal Practices is seeking a self-starting Data Engineer to help shape how a high-impact litigation firm leverages data. In this role, you’ll take ownership of building scalable pipelines and analytics solutions that power decision-making across the organization.    This is a high-growth opportunity for an ambitious engineer who thrives in fast-paced environments and wants to make a direct business impact. Whether you're coming from a high-growth startup or a large organization and looking for more autonomy, this role offers the chance to lead meaningful data projects from day one.    Our ideal candidate is passionate about solving real-world problems with data, communicates clearly, and enjoys building creative, production-ready solutions that drive the business forward.  Responsibilities include, but are not limited to:  Collaborate with stakeholders to gather requirements and develop secure, timely, accurate, trusted, and extensible data models  Seek out opportunities where data can drive impact, and proactively work with teams across the organization to make those solutions a reality  Become a trusted partner to SLP leadership by delivering data solutions that improve operational effectiveness  Design and build scalable, maintainable data pipelines and analytic solutions across various business domains  Support self-service analytics by enabling access to clean, modeled data for analysts and business users  Monitor data quality and availability, implementing processes to ensure reliability of critical datasets  Diagnose and resolve issues in development, staging, and production environments  Provide operational support, including issue investigation, incident response, and remediation  Maintain and extend existing platform management tooling and codebases (e.g., Python, Java, SQL, Ansible, CloudFormation)  Requirements 3+ years of experience in an analytics-focused role (Analytics Engineer, Data Engineer, or Analyst/Consultant with a strong engineering background)  Required: Hands-on experience with Palantir Foundry — strong proficiency expected across pipeline building, ontology/data modeling, and operational deployment  Energized by business impact and a self-starter: you’d rather build an imperfect data model quickly that’s widely used than a perfect model that goes unused  Thrive in cross-functional projects involving both complex technical requirements and user-focused workflows  Expert in SQL and comfortable with Python or other programming languages  Familiarity with modern data stack tools: dbt, Redshift, Looker/Tableau, and other analytics platforms  Preferred Qualifications:  Demonstrated experience using data engineering practices to support generative AI applications  Experience designing pipelines that feed high-quality, well-structured data into LLM-powered workflows  Strong understanding of how to build and serve data models that power automated, AI-driven solutions across business functions  Familiarity with tools and frameworks used in generative AI pipelines (e.g., vector databases, embedding generation, prompt engineering)  Ability to work cross-functionally to identify high-impact automation opportunities using generative AI  Benefits We’re committed to supporting the well-being and success of our team through a robust and thoughtfully designed benefits package, including: 401(k) with Employer Match – Plan for your future with confidence and company support. Health, Dental, and Vision Insurance – Comprehensive coverage to keep you and your family healthy. Short-Term, Long-Term Disability & Life Insurance – Financial protection for life’s unexpected events. Paid Parking – Convenient and covered, so you can focus on your day. Generous Paid Time Off – Ample time to rest, recharge, and take care of personal matters. Employee Referral Program – Earn rewards for introducing talented individuals to our team. Employee Assistance Program (EAP) – Confidential resources for personal and professional support. Employee Discount Program – Access to exclusive savings on a variety of products and services.