Base Skills Required
Strong foundational understanding of Identity and Access Management (IAM) principles — including authentication, authorization, least privilege, and separation of duties.
Deep knowledge of identity relationships across users, service accounts, applications, and entitlements.
Ability to interpret and model identity lifecycle data from multiple systems (e.g., AD, Azure AD, HRIS, PAM, and CIAM).
Experience building and maintaining identity data pipelines and normalization processes.
Competence in SQL, Python, or PowerShell for data querying and automation of identity data extraction and transformation.
Understanding of compliance frameworks such as SOX, PCI-DSS, HIPAA, GDPR, FFIEC, and their connection to identity controls.
Analytical mindset with capability to identify toxic combinations, orphaned accounts, segregation of duties (SoD) violations, and access outliers.
Experience in identity data modeling (hierarchies, joiners/movers/leavers, role/entitlement relationships).
Product & Technical Skills Required
Proficient in Radiant Logic Identity Data Management (IDDM) and Identity Analytics (IDA) — able to build data feeds, perform correlation, and develop identity insights across heterogeneous sources.
Experience defining and calculating Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) tied to IAM objectives such as certification completion, access review timeliness, and privilege recertification.
Ability to visualize identity controls and risks through dashboards using platforms like Power BI, Tableau, Grafana, or Kibana.
Familiarity with SIEM and UEBA( User Entity Behavior Analytics) tools (e.g., Splunk, Exabeam, Microsoft Sentinel) to correlate identity anomalies with threat patterns.
Strong knowledge of security control frameworks (e.g., NIST CSF, ISO 27001, CIS Controls, Zero Trust Maturity Model) and how to measure control adherence quantitatively.
Experience designing and collecting metrics from diverse systems to prove compliance and operational efficiency.
Experience integrating IAM analytics into GRC or IRM tools (e.g., Archer, ServiceNow GRC).
Understanding of data governance, quality scoring, lineage tracking, and data stewardship practices.
Familiarity with APIs, REST interfaces, and data connectors for aggregating identity data.
Soft Skills & Success Factors
Excellent communicator able to explain identity hygiene, data quality, and risk reduction benefits to both technical and non-technical stakeholders.
Skilled at influencing and persuading data owners to participate in data collection and quality improvement initiatives.
Demonstrated ability to translate technical metrics into business-relevant insights — showing how identity analytics drives risk reduction and operational maturity.
Creative thinker with strong data storytelling and visualization abilities to highlight key findings and trends.
Collaborative mindset to work across IAM, Risk, Compliance, IT Ops, and Business teams.
Strong problem-solving, critical thinking, and continuous improvement orientation.
Self-driven, curious, and detail-oriented — with a passion for turning identity data into measurable business value.
Industry certifications such as CISSP, CISM, CISA, Azure Security Engineer, or Identity Governance Specialist are desirable.

Preferred Experience

14+ years of experience in IAM, Security Analytics, or Data Engineering domains.
Proven track record of building identity risk dashboards, control scorecards, or maturity models in large enterprises.
Experience in Zero Trust architecture analytics or Identity Fabric initiatives is a plus.

1. Familiarity with applying Artificial Intelligence (AI) or Machine Learning (ML) techniques in cybersecurity contexts (e.g., anomaly detection, threat hunting, behavioral analytics, or risk scoring).

Experience leveraging AI-enabled tools (such as Copilot for Security, Darktrace, CrowdStrike Charlotte AI, or custom LLM integrations) to enhance detection, response, and automation workflows.
Understanding of LLM safety, prompt engineering, or AI governance frameworks (e.g., NIST AI RMF, EU AI Act readiness) is a plus.
Exposure to data science fundamentals relevant to security (pattern recognition, supervised vs. unsupervised learning, model validation).
Awareness of AI-driven risks (e.g., adversarial ML, data poisoning, model hallucination) and their mitigation within enterprise environments.
Interest in leveraging GenAI for security operations, such as summarizing alerts, drafting reports, or automating incident triage.

Weekly Hours:

40

Time Type:

Regular

Location:

IND:AP:Hyderabad / Argus Bldg 4f & 5f, Sattva, Knowledge City- Adm: Argus Building, Sattva, Knowledge City

It is the policy of AT&T to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, AT&T will provide reasonable accommodations for qualified individuals with disabilities. AT&T is a fair chance employer and does not initiate a background check until an offer is made.

Lead Cybersecurity - Identity Analytics Engineer