Data Operations Lead

AuxoAI•Irvine, CA

1d•Onsite

About The Position

AuxoAI is seeking an AI-native Data Ops Lead to manage the complete health of a client's enterprise data estate. This role acts as a central control point for a hybrid platform, utilizing AIOps and GenAI to automate issue detection, prediction, and resolution, while maintaining human oversight for critical decisions. The position requires a blend of client engagement and technical leadership, serving as the primary contact for client IT and business leadership, managing governance, and overseeing 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 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 (e.g., Fivetran, Airbyte, Matillion).
Strong SQL and data modeling expertise, including dimensional modeling and warehouse performance tuning.
Practical experience with enterprise job scheduling (Tidal, Control-M, Autosys, or equivalent) and with ITSM processes 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 like ServiceNow AIOps/ITOM, Dynatrace, Datadog, or an equivalent in-house build.
Practical grasp of applying ML and GenAI to operations: baselining and anomaly detection on time-series operational data, log and incident clustering, and LLM-assisted triage, incident summarization, or runbook generation.
Excellent written and verbal communication skills.
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 optimization.
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 legacy and modern stacks.
Chair the operational governance cadence, including daily stand-ups, weekly cross-party operations reviews, and monthly business reviews, owning the agenda, decisions, and follow-through.
Own the service scorecard and its narrative, tracking SLA attainment, MTTR, data freshness, repeat-failure rate, pass rate, connector stability, and FinOps.
Protect the scope boundary between run and change, routing enhancement demand through governance and sizing/shaping change requests.
Lead client communication during major incidents and own the post-incident review.
Own the Standard Operating Procedure for the estate, including scope, monitoring framework, severity matrix, RACI, escalation paths, notification matrix, and closure criteria, ensuring it remains current.
Lead triage on P1 and P2 incidents, assigning the resolver domain and running cross-party bridges.
Drive problem management by converting recurring incidents into permanent fixes and reducing the problem backlog.
Set the technical standard for the service across legacy and modern technical stacks.
Own the data architecture standards for the estate and govern the data model, including dimensional design, conformed dimensions, business keys, SCD treatment, and semantic consistency.
Lead diagnosis on major incidents across domain boundaries, distinguishing between ETL faults, database faults, connector faults, source schema changes, report failures, and warehouse contention.
Own all operational decisions and define the monitoring and data-quality framework.
Establish AIOps for the service and advance the service towards predictive and self-healing operations.
Review new business demands, owning the screening and qualification of demand, effort sizing, and implementation roadmap.
Coordinate resolution across third-party hosting providers, SaaS vendors, and internal application teams.
Translate technical faults into business impact for an executive audience.