Senior Director, Data Operations Center (DOC)

Equifax Inc.; Alpharetta (GA); On-site; USD 180,000 - 260,000; Full time

Job summary

Equifax, Inc. seeks an operational architect to lead a Data Operations Center (DOC) in Alpharetta, GA. You will scale data observability across data in motion, guiding a US-based team and partnering with AI leaders to deploy ML anomaly detection and automated remediation.

You will drive incident management, data quality scorecards, and regulatory alignment in a highly regulated environment, delivering rapid, auditable outcomes.

Qualifications

• Bachelor’s degree in Data Science, Data Analytics, CIS, Math, or related quantitative field.
• 7+ years in data operations, systems monitoring, DOC/NOC, data quality engineering, or observability in a regulated industry.
• 5+ years directly managing technical or analytical staff.
• Experience leading digital transformation—from manual checks to automated, alert-driven, self-healing ops.
• Strong familiarity with Snowflake/BigQuery, pipeline observability, and scripting (SQL, Python, dbt).

Responsibilities

• Architect & scale data observability across data in motion: accuracy, completeness, timeliness, and lineage.
• Lead a US-based DOC team; mentor and adopt modern observability tools (e.g., Anomalo).
• Drive automation & AI integration with validation gatekeepers, ML-driven anomaly detection, and remediation scripts.
• Oversee incident management lifecycle; improve MTTD and MTTR, reduce escaped defects.
• Develop Data Quality Scorecards to coach furnishers on formats and compliance.
• Execute complex observability projects with urgency, meet SLAs, and ensure rapid remediation.
• Safeguard regulatory integrity with data governance aligned to FCRA and Metro 2.

Skills

Leadership

Mentoring

Cross-functional collaboration

Problem solving

Strategic thinking

Education

Bachelor’s degree in Data Science or related field

Tools

Anomalo

Monte Carlo

Bigeye

dbt

SQL

Python

Job description

You will be the operational architect and acceleration leader for our Data Operations Center (DOC), taking charge of an already active, tech-enabled control tower that ensures system integrity and data health across our Consumer and Commercial exchanges. While core data contributor teams focus on working in the business, you and your team focus on working on the business—architecting trust, engineering pipeline observability, and scaling automated alerting systems that catch and quarantine defects upstream before they impact downstream products or clients. Directing a team of professionals, you will bridge deep operational engineering with real-time operational vigilance.

This is a high-visibility, transformative leadership role for an operational process innovator. We treat data errors like a security breach, moving beyond manual "firefighting" to a SOC-inspired, AI-enabled control tower powered by advanced anomaly detection, validation gatekeepers, and automated remediation logic. You will step in to propel this transformation forward—scaling key operational metrics (e.g., MTTD, MTTR, Data Uptime), leading a team through ongoing technological evolution, driving furnisher quality advocacy, and maintaining continuous, audit-ready compliance in a heavily regulated landscape.

What You'll Do

• Architect & Scale Data Observability: Lead the expansion of our SOC-inspired control tower, overseeing continuous monitoring of "data in motion" across key dimensions: accuracy, completeness, timeliness, consistency, distribution shifts, volume, schema, and lineage.
• Lead and Develop DOC Team: Empower, mentor, and direct a US-based team of professionals. Coach your team as they deepen their adoption of modern observability tools (e.g., Anomalo), elevate their analytical agility, and build automated, self-healing pipelines.
• Drive Next-Gen Automation & AI Integration: Partner closely with Technology, Data & Analytics, and AI leaders to build on our existing capabilities—deploying validation gatekeepers, ML-driven anomaly detection, and automated remediation scripts (SQL, Python, dbt) to eliminate manual patching and prevent false-positive alert fatigue.
• Command Incident Management & Root Cause Analysis: Oversee the end-to-end incident management lifecycle for data downtime incidents. Establish rigorous operational reviews to improve Mean Time to Detection (MTTD) and Mean Time to Resolution (MTTR), while reducing escaped defect rates.
• Drive Furnisher Advocacy & Quality Scorecards: Translate technical pipeline insights into business strategy by establishing programmatic "Data Quality Scorecards" to coach data furnishers on ingestion formats, Metro 2 guidelines, and commercial compliance standards.
• Execute with Urgency and Ownership: Drive cross-functional execution of complex observability projects on time against key SLAs and metrics. Proactively eliminate uncertainty, allocate resources effectively, and hold your team accountable for rapid incident resolution and low anomaly recurrence.
• Safeguard Regulatory & Enterprise Integrity: Serve as a trusted steward of data governance, ensuring all monitoring frameworks, quarantine procedures, and system controls adhere strictly to FCRA, Metro 2, and commercial regulatory guidelines.

What Experience You Need

• Bachelor’s degree in Data Science, Data Analytics, Computer/Management Information Systems, Mathematics, Statistics, Engineering, or a related quantitative field.
• 7+ years of progressive leadership experience in data operations, systems monitoring, site reliability/operations center management (DOC/SOC/NOC), data quality engineering, or data observability within a regulated industry.
• 5+ years directly managing technical or analytical staff, with a proven track record of guiding, upskilling, and developing senior individual contributors (e.g., Data Observability Analysts, Data Engineers).
• Proven experience stepping into an ongoing operational or digital transformation—transitioning teams from manual data checking to automated, alert-driven, and self-healing operational models.
• Strong familiarity with enterprise data architectures, cloud data warehouses (Snowflake, BigQuery), pipeline observability platforms (Monte Carlo, Anomalo, Bigeye), and scripting frameworks (SQL, Python, dbt).

What Could Set You Apart

• Direct experience designing or scaling a Data Operations Center (DOC) utilizing ML-driven anomaly detection, automated self-healing logic, or AI agentic workflows.
• Proven success managing external data provider compliance through programmatic Data Quality Scorecards and SLA enforcement.
• Deep domain expertise spanning Consumer and Commercial credit reporting standards (FCRA, Metro 2).