Lead Data Engineer

Company Overview Docusign brings agreements to life. More than 1.8 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using the Docusign AI-native IAM platform, companies can create, commit, and manage agreements with solutions created by the 1 company in e-signature and contract lifecycle management (CLM). What you'll do As the Lead Assignment Engine Data Engineer, you will lead the data engineering design for an explainable, configurable engine that converts normalized cloud and operational data into actionable cost attribution. You will define how shared pools such as observability, storage, networking, SQL, and data platforms are assigned to the products and services that consume them. You will operate at the boundary of data engineering, FinOps, Finance, and platform architecture. The role requires strong technical judgment, the ability to turn ambiguous allocation questions into durable data products, and the leadership to establish patterns that other engineers can extend. This position is an individual contributor role reporting to the Senior Director, Engineering. Responsibility

• Build batch and/or streaming pipelines for Azure and GCP billing data, with an architecture that can extend to additional cloud providers
• Normalize provider-specific billing and usage data into a consistent, FOCUS-aligned model
• Ingest and join resource inventory, tagging, ownership, product, service, and environment metadata
• Implement reconciliation checks between source billing systems, the normalized warehouse, and downstream reporting datasets
• Establish data contracts, schemas, lineage, freshness checks, completeness checks, and failure-handling patterns
• Create reusable transformations that support executive dashboards, product-level cost views, burn-rate reporting, and anomaly detection
• Build operational visibility for pipeline health, including freshness, latency, volume, schema drift, and failed-record monitoring
• Partner with platform, FinOps, Finance, and service-owning teams to resolve data-quality gaps and improve attribution coverage
• Document pipeline behavior, ownership, runbooks, recovery procedures, and known data limitations
• Improve automation, test coverage, deployment safety, and cost efficiency of the data platform
• Build or integrate event-driven ingestion patterns so new Cost Management exports trigger processing without manual refreshes
• Participate in on-call rotations and contribute to improving system reliability and incident response
• Support Microsoft Fabric, Data Lake, Data Factory, and KQL-based consumption patterns where they fit the target architecture Job Designation Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation) Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law. What you bring Basic
• 12+ years of professional experience designing and delivering production data platforms or data products, including experience leading technical design for complex data systems
• Experience with SQL and Python or an equivalent data-engineering language
• Experience with data modeling, ELT/ETL architecture, orchestration, testing, observability, and operational ownership
• Experience translating business rules into scalable, configurable, and auditable data-processing systems
• Experience leading technical decisions across multiple teams and stakeholders Preferred
• FinOps, cloud-cost allocation, showback/chargeback, cost-to-serve, or unit-economics systems
• Multi-cloud billing normalization and FOCUS-aligned data models
• Shared-cost allocation across observability, storage, networking, databases, or data platforms