{"id":1550858,"url":"https://alion.io/job/tko-group-holdings-inc-sr-data-engineer","title":"Sr. Data Engineer","company":{"id":1959663,"name":"TKO Group Holdings","domain":"tkogrp.com","url":"https://alion.io/company/tkogrp","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":81,"open_postings":9,"ghost_share":0,"stale_share":0.778,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-10-21T05:46:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":144552,"max":null,"currency":"USD","period":"year","gross":null,"usd_annual":144552},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Datadog","optional":false},{"name":"dbt","optional":false},{"name":"Feature Store","optional":false},{"name":"Fivetran","optional":false},{"name":"GitHub Actions","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"Master Data Management","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"SSIS","optional":false},{"name":"Terraform","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-09-30T00:00:00Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-21T20:47:35Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"Who We Are:\n\nTKO Group Holdings, Inc. (NYSE: TKO) is a premium sports and entertainment company. TKO owns iconic properties including UFC, the world’s premier mixed martial arts organization; WWE, the global leader in sports entertainment; and PBR, the world’s premier bull riding organization. Together, these properties reach 1 billion households across 210 countries and territories and organize more than 500 live events year-round, attracting more than three million fans. TKO also services and partners with major sports rights holders through IMG, an industry-leading global sports marketing agency; and On Location, a global leader in premium experiential hospitality.\n\nThe Senior Data Engineer, Data Platform is a hands-on engineering role at the center of TKO's data estate. Day to day, the role owns pipelines, data models, and Snowflake infrastructure, and resolves the issues that surface across TKO's data workflows. Over the next two years, the role also helps build the platform those workflows will move onto.\nTKO today runs several data platforms side by side - separate Snowflake accounts, two clouds, three orchestrators, and four reporting tools - a product of bringing UFC, WWE, PBR, IMG, and On Location together under one company. The Platform Unification program consolidates that estate onto a single reference architecture: one Snowflake account, dbt for transformation, Prefect on Kubernetes for orchestration, Terraform for infrastructure, GitHub Actions with OIDC for delivery, and governance applied as code rather than by convention. This role is one of the engineers who builds it. The emphasis is deliberately on platform depth rather than depth in any one business unit. We are looking for an engineer who has operated a shared data platform used by many teams - someone who thinks in terms of reusable frameworks, access models, warehouse cost, blast radius, and the paved path that keeps other teams from inventing their own - and who can take a standard built in one corner of the estate and make it hold across UFC, WWE, PBR, IMG, and On Location. Business-unit domain knowledge will be built on the job. The platform engineering judgment needs to arrive with the person.\nThe role sits within TKO's Enterprise Data organization, reports to the Manager, Data Engineering, and works closely with DevOps, Data Science, Analytics Engineering, Cybersecurity, IT, and the global delivery partners supporting the brand.\nRoles and Responsibilities\nPlatform Ownership and Reliability\nOwn pipelines, data models, and platform reliability end to end, covering all ingress and egress paths into and out of Snowflake.\nAct as an escalation point for platform incidents - triage orchestration, transformation, and monitoring alerts, run root cause analysis, and close issues out with permanent fixes rather than recurring manual intervention.\nDefine and hold service levels for the pipelines the business depends on: freshness, job success rate, and time to recover. Report honestly against them.\nProvide debug, triage, support, and design guidance to engineers and analysts working anywhere in the platform.\nShare in the team's on-call and event-support rotation, including heightened coverage around major live events.\nPlatform Unification and the North Star Architecture\nDesign and build the shared components of TKO's unified platform: the canonical medallion layering, the role and access taxonomy, the shared ingestion library, and the orchestration framework every business unit will run on.\nLead Snowflake consolidation work as the separate accounts converge - cross-region replication, account merges, re-pointing secure shares, parity validation, and cutover.\nRetire duplicated tooling across the estate rather than relocating it: self-managed Airflow, notebook-driven ingestion, SSIS, and legacy CI/CD.\nBuild golden paths - templates, reference implementations, and documentation - so business unit teams and delivery partners can onboard a source or ship a model without a ticket to this team for every change.\nIngestion and Orchestration\nExtend and maintain a shared Python ingestion library: reusable connectors for SFTP, database, API and file sources; S3 landing with Snowflake COPY and Snowpipe sinks; watermarking and incremental logic; registry and observability hooks.\nRun managed ingestion (Fivetran) as the default for supported SaaS sources, and make the build-versus-buy call where no connector exists.\nOwn Prefect on EKS - deployments, work pools, worker health, containerized flows - and the standard flow taxonomy the whole organization writes against.\nConsolidate connector logic that exists today into a shared collect/ingest library, instead of maintaining three versions of the same capability.\nSnowflake Engineering and Cost\nAdminister Snowflake as a shared, multi-tenant platform: warehouse sizing and workload isolation, resource monitors, query and clustering optimization, replication, and secure sharing with external partners.\nManage Snowflake objects declaratively in Terraform so that structure and access are reviewable, reproducible, and auditable rather than accumulated through ad hoc SQL.\nOwn warehouse cost. Find the queries, models, and schedules driving consumption, fix them, and give leadership a defensible view of where credits are going.\nInfrastructure as Code and Delivery\nBuild and maintain AWS infrastructure with Terraform across the TKO accounts - EKS, networking, IAM, S3, Lambda, and secrets management - operating at the workload layer inside central IT's guardrails rather than standing up a parallel estate.\nMaintain CI/CD on GitHub Actions with AWS OIDC and no static credentials, including the dev, UAT, and production promotion path, required-reviewer gates, and the dbt tests and data quality checks that block a bad change before it lands.\nRaise the engineering bar through code review, testing standards, and documentation that holds up after the author moves on.\nTransformation and Data Modeling\nBuild and review dbt models against the canonical medallion, and merge the organization's separate macro libraries into one shared set - identity resolution and feature engineering from the consumer side, policy generation from the brand side.\nPartner with Analytics Engineering and Data Science so semantic models, feature pipelines, and reporting sit on governed, versioned datasets rather than one-off extracts.\nGovernance and Security, Applied as Code\nImplement masking, tokenization, and row-access policies as code across the estate - encrypted at landing, decrypted at the marts - so PII is handled the same way everywhere it lands.\nMaintain network policies, secrets and key rotation, and SSO-driven role provisioning; support access reviews, audit requests, and remediation of security findings.\nSupport cataloging and lineage tooling, and build to the standards set by TKO's Master Data Management and Governance function.\nEnabling Analytics, ML, and AI\nKeep the platform ready for the workloads built on top of it: in-platform model training and serving, feature pipelines, and the curated, retrieval-ready datasets that AI applications depend on.\nUse AI-assisted engineering tooling in daily development and help establish sensible team practice around it.\nCollaboration and Technical Influence\nWork directly with the global delivery partners - mentoring them onto the target stack through their migration waves and reviewing their work against platform standards.\nMentor engineers on the team and serve as a technical reference point on design decisions, without formal management responsibility.\nExplain technical trade-offs plainly to stakeholders who are not engineers, and to leadership.\nRequired Experience\n7+ years building and operating production data platforms, including at least 3 years weighted toward shared platform and infrastructure work rather than single-team pipeline delivery.\nDeep hands-on Snowflake experience at an administrator level: RBAC design, warehouse sizing and cost management, masking and row-access policies, replication, secure data sharing, and performance tuning.\nStrong Python and SQL, with the ability to write and maintain a shared library other engineers build against - not only scripts and DAGs.\nProduction experience with a modern orchestrator (Prefect, Airflow, or Dagster), including running and upgrading it on Kubernetes or comparable infrastructure rather than only consuming a managed service.\nHands-on infrastructure as code with Terraform on AWS: EKS or ECS, IAM, VPC networking, S3, Lambda, and secrets management.\ndbt at scale - project structure, macros, testing, and the judgment to consolidate overlapping projects rather than accumulate them.\nOwnership of CI/CD for data systems, GitHub Actions preferred, including keyless OIDC authentication, multi-environment promotion, and automated quality gates.\nExperience with managed ingestion (Fivetran or comparable), CDC, Snowpipe, and event or behavioral data platforms such as Amplitude or Segment.\nOperational discipline: observability tooling such as Datadog, alerting design, incident response, and post-incident follow-through.\nDemonstrated work on a platform migration or consolidation of real scope - merging warehouse accounts, replacing an orchestrator, or retiring a legacy stack - including the parity validation and cutover planning that determines whether one succeeds.\nA record of setting standards other engineers actually adopt: reusable frameworks, reference implementations, documentation, and code review.\nComfortable working alongside offshore and partner delivery teams and raising their output to the platform's standards.\nClear written communication, particularly design documents, runbooks, and incident write-ups.\nPractical experience meeting data governance requirements - PII classification, access controls, retention, and lineage - preferred.\nExposure to ML or AI workloads running on the data platform, such as in-warehouse training and serving, feature stores, or retrieval pipelines, preferred.\nExperience in media, sports, entertainment, live events, ticketing, or consumer and fan data environments preferred.\n\nPer local requirements and in the interest of transparency, the hourly rate shown below reflects the prevalent current hiring range for this position. Hiring pay rates are based on a number of factors, including location and may vary depending on job-related qualifications, knowledge, skills and experience. The company strives to provide locally competitive rewards packages, which include base rate along with, as applicable, short- and long-term incentives, growth and developmental opportunities, and robust benefits, such as health care, retirement, vacation and other paid time off, and additional offerings.\n\nHiring Rate Minimum:\n$144,552 annually (minimum will not fall below the applicable State/local minimum salary thresholds)\nHiring Rate Maximum:\n$192,736 annually\n\nTKO is an Equal Opportunity Employer and complies with all applicable federal, state, and local laws regarding non-discrimination in employment. TKO makes employment decisions based on merit and qualifications, without considering an employee’s or applicant’s race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, marital status, veteran status, or any other basis prohibited under federal, state or local laws governing non-discrimination in employment in every location in which the Company has facilities. TKO also provides reasonable accommodations for qualified individuals with disabilities in accordance with the Americans with Disabilities Act (ADA) and applicable state or local laws. For information about Privacy and Information Security for TKO employment candidates, please review our Privacy Policy. 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