Senior Data Engineer

WME | William Morris Endeavor•Beverly Hills, CA

1d•$127,500 - $170,000

About The Position

The Senior Data Engineer owns and modernizes WME Enterprise IT's SQL data estate — the databases behind the custom applications the product and engineering teams build, the legacy SQL environment those applications run on, and the pipelines that turn that data into trusted, query-ready information for analytics and reporting. This role does not inherit a modern, greenfield data platform; it inherits a working legacy SQL environment and helps set and drive the strategy to stabilize and modernize it. The role manages and supports the databases behind the custom product and engineering applications, keeps that data highly available with sound disaster-recovery planning, and builds the pipelines and analytics-ready data that reporting and BI are built on. It brings deep SQL and SQL Server expertise, works within the enterprise data architecture standards set by the Principal Enterprise Architect, partners with the Senior DBA on the database estate — developing toward broader database ownership over time — and partners with the Sr Manager, GRC on data governance and privacy. WME operates across a global footprint spanning the Americas, EMEA, and APAC, so the role must know how to operationalize data-privacy and regulatory requirements — GDPR, CCPA, and regional equivalents — at the data layer. The data it delivers serves analytics, reporting, and — increasingly — AI. It is a deep-technical senior individual-contributor role without direct reports. How This Team Works Enterprise IT runs on one idea: operational excellence — systems that just work, problems fixed at the root, and an engineering bar that keeps climbing. Trusted data is foundational to that, and this role holds the bar for it. Six operating foundations describe how the function works, and a Senior engineer here models all six. Service management, done right. Runs the SQL data estate as a dependable service with clear ownership, SLAs, and root-cause fixes. Continual improvement. Uses data-quality signals, usage, and business feedback to keep improving pipelines and reporting. AI fluency. Uses AI tooling fluently in data engineering, and builds the trusted data foundation the AI backbone relies on. Ownership and documentation. Owns the data estate and pipelines end to end and leaves data models, runbooks, and documentation that scale. Security and risk by default. Builds data quality, classification, and access control in by default, in partnership with governance. Clarity and influence. Translates business questions into data products, and partners credibly with architecture, governance, and the business.

Requirements

7+ years in data and/or database engineering, with progressive responsibility.
Deep SQL and SQL Server administration — T-SQL, query optimization, maintenance, and troubleshooting of application databases.
Experience modernizing legacy SQL / database environments — consolidation, upgrades, and migration.
Database high availability and disaster recovery — Always On / clustering, backup and restore, and recovery planning (RTO / RPO).
ELT / pipeline engineering and integration from multiple source systems, including databases behind custom applications.
Data modeling and analytics-ready datasets; Power BI semantic models / datasets (the data layer behind reports) a plus.
Foundational data governance, and experience operationalizing data-privacy regulation — GDPR, CCPA, or equivalents — at the data layer (residency, retention, subject rights).

Nice To Haves

Microsoft certifications — Azure Database Administrator (DP-300), Azure Data Engineer (DP-700 / DP-203), or SQL Server administration.
Data-privacy certification (e.g., IAPP CIPP/E or CIPP/US) or hands-on GDPR / CCPA operational experience.
Database HA/DR technologies (Always On, log shipping, replication) and backup / recovery tooling.
SAP data integration experience; data catalog / governance tooling (Microsoft Purview or equivalent).
Background in media, entertainment, or other high-confidentiality, multi-region environments.

Responsibilities

Support, stabilize, and maintain the legacy SQL databases behind the custom product / engineering applications — performance, reliability, and availability.
Help set and drive the strategy to modernize the legacy SQL environment — consolidation, upgrades, and migration toward a governed, better-managed data estate.
Partner with the Principal Enterprise Architect (#55) on the database and data side of legacy modernization.
Build and modernize pipelines from source systems — the custom applications, SAP, and business systems — into a governed data store.
Own pipeline orchestration and scheduling; engineer resilient, observable pipelines with clear failure handling and recovery.
Integrate SAP as a major source, in partnership with the SAP program.
Build the data models and analytics-ready datasets that BI, reporting, and analytics are built on.
Provide well-structured, trustworthy data so the business and analysts can build their own reports — deliver the data behind the reports, not the reports themselves.
Serve analytics, reporting, and AI consumers of the data.
Ensure the databases and data estate are highly available — redundancy, failover, and resilience matched to each system's criticality.
Own database backup, restore, and disaster-recovery planning and testing.
Set and meet recovery objectives (RTO / RPO) for critical data, in partnership with infrastructure and BCDR.
Contribute at a foundational level to data governance — quality, classification, lineage, and stewardship — in partnership with the GRC function.
Operationalize data-privacy and regulatory requirements at the data layer — GDPR, CCPA, and regional equivalents — across WME's global footprint: data residency, retention, minimization, and support for data-subject rights.
Implement the data controls GRC defines, including SOX-relevant controls, and produce the supporting evidence.
Bring deep SQL Server administration to the estate — T-SQL, tuning, maintenance, and troubleshooting.
Develop database-administration depth through structured mentorship from the Senior DBA, growing toward broader database ownership over time.
Maintain runbooks, documentation, and knowledge transfer; apply AI-assisted engineering where it adds value.