Contract: Senior Data Engineer

<p>Upwork ($UPWK) is the world’s work marketplace. We serve everyone from one-person startups to over 30% of the Fortune 100 with a powerful, trust-driven platform that enables companies and talent to work together in new ways that unlock their potential.  </p> <p>Last year, more than $3.8 billion of work was done through Upwork by skilled professionals who are gaining more control by finding work they are passionate about and innovating their careers. </p> <p>This is an engagement through Upwork’s Hybrid Workforce Solutions (HWS) Team. Our Hybrid Workforce Solutions Team is a global group of professionals that support Upwork’s business. Our HWS team members are located all over the world.</p> <hr> <p>This hybrid engagement will help build and operate Data Platform as a Service capabilities for internal teams. The role focuses on enabling scalable, secure, reliable, and well-governed data products through platform engineering practices—CI/CD for data, data mesh enablement, automation, observability, and self-service workflows. This engineer will partner closely with data engineering, analytics, and AI teams to improve platform reliability, developer experience, and time-to-delivery.</p> <h3><strong>Work/Project Scope:</strong></h3> <ul> <li>Build and operate platform services that enable teams to deliver data products reliably (pipelines, transformations, orchestration, metadata, governance).</li> <li>Design and implement CI/CD for data (tests, deployments, promotion workflows, rollback strategies, versioning).</li> <li>Improve data platform reliability through observability, SLAs/SLOs, alerting, incident response, and runbooks.</li> <li>Enable data mesh patterns: domain ownership, standardized interfaces, reusable templates, and paved paths.</li> <li>Develop internal tooling and automation for onboarding datasets, creating standardized pipelines, and enforcing best practices (quality, security, lineage).</li> <li>Implement or enhance data quality and validation frameworks (contract testing, reconciliation, anomaly detection).</li> <li>Optimize platform performance and cost (warehouse optimization, job efficiency, resource scaling).</li> <li>Collaborate with Security/Compliance to ensure encryption, access control, auditability, and least-privilege practices.</li> <li>Partner with AI teams to ensure data products are fit for AI/ML workloads (feature readiness, dataset versioning, reproducibility, governance).</li> <li>Improve and maintain Airflow orchestration, including DAG design, dependency management, and operational reliability for dbt and analytics workflows.</li> </ul> <h3><strong>Must Haves (Required Skills):</strong></h3> <ul> <li>Strong software engineering foundation building production systems (Python and/or Rust preferred; strong APIs/services mindset).</li> <li>Proven experience in data platform engineering (not just building pipelines—building platforms for others).</li> <li>Hands-on experience with CI/CD, Infrastructure as Code, and automation.</li> <li>Experience with observability and reliability engineering (metrics, logs, tracing, SLOs, on-call readiness).</li> <li>Strong knowledge of modern data ecosystem patterns (data modeling, orchestration, warehousing/lakehouse concepts).</li> <li>Practical experience enabling data mesh or self-service platform capabilities.</li> <li>Ability to work across ambiguity, drive delivery, and influence standards.</li> </ul> <h3><strong>Preferred Background/Experience:</strong></h3> <ul> <li>Experience with Snowflake and modern orchestration/testing patterns (dbt/SQLMesh-like workflows, St ... (truncated, view full listing at source)