Quick overview
Job description
Position Overview
Job Responsibilities
- Lead the design and delivery of a new, efficient and scalable data model, and plan the transition including 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, and drive 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 that powers 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
- Leveraging AI assisted tools to enhance productivity, streamline workflows, and support day-to-day activities
- Other duties as assigned
Required Qualifications
- 8+ years of experience in data engineering, with a track record of designing and owning data models for complex, production SaaS products with high scale and low latency
- Deep expertise in data modeling, including relational, dimensional, and entity or graph-based approaches, and in choosing the right structure 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, including batch and streaming processing, using tools such as Spark, Kafka, Airflow, Glue, or similar.
- Experience with modern data platforms, including warehouses and lakehouses, and with cloud platforms, preferably AWS
- Solid understanding of data governance, data quality, and row- and field-level security and access control models with experience implementing data quality testing, monitoring, and observability for data pipelines and data assets.
- Experience designing semantic layers or data models that power analytics, BI, and AI or LLM-based applications
- Demonstrated ability to drive architecture decisions across teams, write clear design documents, and scope and plan multi-quarter initiatives
- Strong communication skills to effectively collaborate with diverse teams, including product managers, engineers, data scientists, and customer-facing teams
- Familiarity with contract lifecycle management, legal tech, or document-heavy enterprise domains is a plus
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