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 a Senior Data Engineer, you will build the foundational pipelines that bring together multi-cloud billing and operational data. You will make data dependable, traceable, and easy for downstream teams to consume by supporting cost visibility, budget monitoring, anomaly detection, shared-cost allocation, and cost-to-serve reporting. This is an execution-oriented engineering role with end-to-end ownership of pipeline components, including source onboarding, normalization, transformation, validation, monitoring, incident response, and documentation. This 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 Support Microsoft Fabric, Data Lake, Data Factory, and KQL-based consumption patterns where they fit the target architecture Treat private endpoints, private DNS, managed identities, Entra ID/RBAC, and disabled public access as part of the data-platform design—not afterthoughts Participate in on-call rotations and contribute to improving system reliability and incident response 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 5+ years of professional experience designing, implementing, and operating production data pipelines Strong SQL and Python or an equivalent data-engineering language Experience with cloud billing, usage, infrastructure, telemetry, or other operational datasets Experience with dimensional modeling, data warehouses/lakes, ETL/ELT, and data-quality testing Practical experience with pipeline orchestration, monitoring, alerting, and incident response Preferred Multi-cloud billing data or FinOps data engineering FOCUS, cloud-cost normalization, resource tagging, or showback/chargeback systems Azure, GCP, BigQuery, Microsoft Fabric, Azure Data Factory, Data Lake Storage, KQL, or comparable warehouse technologies Event Grid or equivalent event-driven ingestion, managed identities, Entra ID/RBAC, private endpoints, private DNS, and network-isolated data services Data lineage, schema evolution, reconciliation, anomaly detection, deployment verification, or finance-system integration Ability to work across engineering and business stakeholders to clarify data definitions and ownership Life at Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live. Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at accommodations@docusign.com. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance. Applicant and Candidate Privacy Notice #LI-Hybrid #LI-VP3 As a Senior Data Engineer, you will build the foundational pipelines that bring together multi-cloud billing and operational data. You will make data dependable, traceable, and easy for downstream teams to consume by supporting cost visibility, budget monitoring, anomaly detection, shared-cost allocation, and cost-to-serve reporting. This is an execution-oriented engineering role with end-to-end ownership of pipeline components, including source onboarding, normalization, transformation, validation, monitoring, incident response, and documentation. This 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 Support Microsoft Fabric, Data Lake, Data Factory, and KQL-based consumption patterns where they fit the target architecture Treat private endpoints, private DNS, managed identities, Entra ID/RBAC, and disabled public access as part of the data-platform design—not afterthoughts Participate in on-call rotations and contribute to improving system reliability and incident response Basic 5+ years of professional experience designing, implementing, and operating production data pipelines Strong SQL and Python or an equivalent data-engineering language Experience with cloud billing, usage, infrastructure, telemetry, or other operational datasets Experience with dimensional modeling, data warehouses/lakes, ETL/ELT, and data-quality testing Practical experience with pipeline orchestration, monitoring, alerting, and incident response Preferred Multi-cloud billing data or FinOps data engineering FOCUS, cloud-cost normalization, resource tagging, or showback/chargeback systems Azure, GCP, BigQuery, Microsoft Fabric, Azure Data Factory, Data Lake Storage, KQL, or comparable warehouse technologies Event Grid or equivalent event-driven ingestion, managed identities, Entra ID/RBAC, private endpoints, private DNS, and network-isolated data services Data lineage, schema evolution, reconciliation, anomaly detection, deployment verification, or finance-system integration Ability to work across engineering and business stakeholders to clarify data definitions and ownership
