Data Operations Lead

AuxoAI•Irvine, CA

1d•Hybrid

About The Position

We are looking for an AI-native Data Ops Lead to own the end-to-end health of a client's enterprise data estate, operating as the single accountable control tower across a hybrid platform. What distinguishes this role is how you get there: you will build a service that detects, correlates and increasingly resolves on its own, applying AIOps and GenAI to compress triage, predict failure on the critical path, and convert repetitive manual intervention into automation — while keeping human judgment at the decision gates that matter. The role requires the candidate to exhibit engagement and technical leadership in equal measure. You will be the face of the service to client IT and business leadership, run the governance cadence and hold the delivery relationship.

Requirements

Bachelor’s/Master’s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
10–15 years in data engineering, data warehousing or data operations, with experience of working in managed service or Data Operations function.
Demonstrated experience as a client-facing lead — running governance forums, owning SLAs and service reporting, and managing escalations with senior IT and business stakeholders.
Experience in Informatica PowerCenter and a relational data warehouse platform.
Working expertise across a modern cloud data stack — Snowflake, dbt, and a managed ingestion tool such as Fivetran, Airbyte or Matillion.
Strong SQL and data modelling expertise, including dimensional modelling and warehouse performance tuning.
Practical experience with enterprise job scheduling (Tidal, Control-M, Autosys or equivalent) and with ITSM process in ServiceNow — incident, problem, change and major incident management.
Proven ability to define and operate a monitoring and alerting framework that measures data delivery, not just job execution.
Experience implementing AIOps or intelligent observability in a production environment —using platforms such as ServiceNow AIOps/ITOM, Dynatrace, Datadog or an equivalent in-house build.
Practical grasp of how to apply ML and GenAI to operations: baselining and anomaly detection on time-series operational data, log and incident clustering, and LLM-assisted triage, incident summarisation or runbook generation.
Experience coordinating resolution across third-party hosting providers, SaaS vendors and internal application teams.
Excellent written and verbal communication — able to translate a technical fault into business impact for an executive audience.
Based in or willing to relocate to the Irvine / Los Angeles area, with the ability to be onsite with the client as the engagement requires.

Nice To Haves

Working knowledge of MicroStrategy or a comparable enterprise BI platform.
Familiarity with Python or PySpark for operational tooling and automation.
Exposure to cloud cost management and FinOps practice, particularly Snowflake credit optimisation.
Experience introducing observability, alert-quality review and toil reduction into an inherited support estate.
ITIL certification or equivalent practical grounding in service management.

Responsibilities

Act as the primary point of accountability to client IT leadership and business data owners for the end-to-end data service across both the legacy and modern stacks.
Chair the operational governance cadence — daily stand-up, weekly cross-party operations review, , monthly business reviews — and own the agenda, decisions and follow-through.
Own the service scorecard and its narrative: SLA attainment, MTTR, data freshness, repeat-failure rate, pass rate, connector stability and FinOps.
Protect the scope boundary between run and change: route enhancement demand through governance, size and shape change requests.
Lead client communication during major incidents and own the post-incident review.
Own the Standard Operating Procedure for the estate — scope, monitoring framework, severity matrix, RACI, escalation paths, notification matrix and closure criteria — and keep it current as the platform evolves.
Lead triage on P1 and P2 incidents, assign the resolver domain, run the cross-party bridge
Drive problem management — convert recurring incidents into permanent fixes, and burn down the problem backlog
Set the technical standard for the service across legacy and modern technical stacks.
Own the data architecture standards for the estate, govern the data model — dimensional design, conformed dimensions and business keys, SCD treatment, and semantic consistency
Lead diagnosis on major incidents across domain boundaries — distinguishing an ETL fault from a database fault, a connector fault from a source schema change, a report failure from warehouse contention.
Own all operational decisions, define monitoring and data-quality framework
Establish AIOps for the service, advance the service toward predictive and self-healing operations
Review new business demands, own screening and qualifications of the demand, effort sizing and implementation roadmap