Staff Data Engineer - Agiloft

About Agiloft

Agiloft is the global leader in data-first contract lifecycle management (CLM) software. We help organizations manage the end-to-end process of proposing, negotiating, signing, and leveraging contracts using our flexible Data-first Agreement Platform (DAP). Recognized as a leader by top analysts like Gartner, Forrester, and IDC, our no-code platform allows business users to seamlessly manage contract lifecycles while utilizing pragmatic artificial intelligence as a legal force multiplier.

Our working philosophy is "EX = CX": when employee experience is excellent, so is customer experience. We foster an inclusive environment with multiple Employee Resource Groups (ERGs) and prioritize work/life balance through floating holidays and quarterly, no-questions-asked wellness days.

Position Overview

We are looking for an experienced Staff Data Engineer to lead the next generation of our contract data model. In this role, you will collaborate with Product and Engineering to design and deliver a new, more efficient data model that serves as the foundation for contract intelligence, AI-extracted data, and analytics across our CLM platform. You will play a key role in defining the go-forward data model and system of record, leading its implementation from initial rollout through expanded adoption.

Key Responsibilities

• Lead the design and delivery of a new, efficient, and scalable data model, planning transition and migration strategies that minimize disruption for existing customers.
• Partner with Product and Engineering leadership to define the long-term data architecture and system of record, driving key architectural decisions across teams.
• Design, build, and maintain scalable data pipelines and data flows across core applications, AI services, and analytics systems.
• Build and evolve the data warehouse and analytics layer powering customer insights, workflow automation, and AI-driven features.
• Ensure data platforms meet high standards for performance, security, privacy, compliance, and cost efficiency.
• Implement monitoring and automated data quality checks to keep pipelines and data assets reliable and trustworthy.
• Establish data governance standards, including documentation, lineage, and retention.
• Collaborate with Data Science, ML, Professional Services, and customer-facing teams on analytics, integrations, and data migrations.
• Provide technical guidance and mentorship, lead design and code reviews, and contribute to the data roadmap.
• Stay current with advancements in data engineering, data modeling, and cloud data platforms to identify and implement best practices.

Required Qualifications

• 8+ years of experience in data engineering, with a proven track record of designing and owning data models for complex, production SaaS products operating at high scale and low latency.
• Deep expertise in data modeling (relational, dimensional, entity, or graph-based approaches) and selecting the optimal structures for scale, flexibility, and performance.
• Proven experience leading large-scale schema redesigns or data migrations in live, multi-tenant environments with minimal customer disruption.
• Hands-on experience building and operating production data pipelines (batch and streaming) using tools such as Spark, Kafka, Airflow, Glue, or similar.
• Experience with modern data platforms (warehouses/lakehouses) and cloud infrastructure, preferably AWS.
• Solid understanding of data governance, data quality, and row- and field-level security/access control models, alongside experience implementing data observability and quality testing.
• Experience designing semantic layers or data models powering analytics, BI, and AI/LLM-based applications.
• Demonstrated ability to drive architectural decisions across teams, author clear technical design documents, and scope multi-quarter initiatives.
• Strong communication skills to effectively collaborate across product managers, engineers, data scientists, and customer-facing teams.

Nice to Have

• Familiarity with contract lifecycle management (CLM), legal tech, or document-heavy enterprise domains.