Job Summary
The AI Data Specialist role focuses on designing and optimizing secure data platforms using Azure Databricks and related services to support advanced analytics and AI driven insights for global stakeholders. The specialist will configure and manage Databricks Unity Catalog workflows Delta Lake and Databricks SQL to ensure reliable data pipelines and governed data access. The role demands strong hands on engineering expertise excellent collaboration with cross functional data teams and a co
Responsibilities
- Design and optimize scalable data engineering solutions on Azure Databricks that enable advanced analytics and AI workloads for enterprise stakeholders and downstream applications
- Configure and administer Databricks Unity Catalog to establish centralized governance robust access controls and consistent data security policies across all workspaces and environments
- Develop automate and monitor Databricks Workflows that orchestrate complex data pipelines ensuring reliable scheduling error handling and recovery for critical business processes
- Implement and manage Databricks Delta Lake structures to support incremental data ingestion versioning and time travel capabilities that improve data quality and analytical flexibility
- Build fine tuned Databricks SQL queries dashboards and data models that deliver actionable insights to business teams while maintaining efficient performance on large datasets
- Collaborate with data scientists analysts and application teams to translate analytical requirements into well structured data solutions that accelerate experimentation and deployment
- Document data models pipeline logic governance rules and operational procedures in a clear concise manner to promote transparency maintainability and knowledge sharing across teams
- Apply rigorous data quality checks validation frameworks and monitoring practices to ensure that curated datasets remain accurate consistent and fit for AI and reporting use cases
- Partner with cloud infrastructure and security teams to align Databricks configurations networking and identity controls with enterprise standards and regulatory expectations
- Optimize cluster configuration storage layout and resource utilization in Azure Databricks to balance performance reliability and cost efficiency for production and development workloads
- Contribute to continuous improvement initiatives by evaluating new Databricks features and best practices then guiding adoption that strengthens data platform capability and resilience
- Support domain oriented data solutions that can incorporate life and annuities insurance concepts where relevant helping business teams better understand risk policy behavior and customer outcomes
- Champion responsible and ethical use of data by promoting governance practices that protect customer information support regulatory compliance and enhance societal trust in data driven decisions
Qualifications
- Demonstrate strong hands on expertise in Azure Databricks including consistent practical experience with Databricks Unity Catalog Workflows SQL and Delta Lake in complex environments
- Bring solid experience in designing and operating large scale data pipelines with a minimum of nine years in data engineering or AI data specialist roles and exposure to modern cloud data stacks
- Show familiarity with life and annuities insurance data structures processes and metrics as a beneficial domain skill that can deepen the impact of analytics solutions for insurance stakeholders
- Exhibit advanced proficiency in structured query development performance tuning and data modeling practices that support reporting machine learning and self service analytics
- Apply sound knowledge of data governance security and compliance concepts to configure Unity Catalog permissions data classification and audit readiness for enterprise usage
- Demonstrate strong problem solving and debugging skills to investigate pipeline failures data anomalies and performance bottlenecks while driving sustainable corrective actions
- Communicate clearly with technical and nontechnical partners providing explanations of data solutions trade offs and risks that support informed decision making and collaboration
- Manage work effectively in an office based day shift model by coordinating with cross functional teams aligning priorities and ensuring consistent availability for stakeholder interactions
Certifications Required
Databricks Data Engineer Professional Certification or Azure Data Engineer Associate certification preferred
Über Cognizant
Cognizant (NASDAQ: CTSH) i ist ein Technologiedienstleister und Entwickler von KI-Lösungen. Wir schlagen die Brücke zwischen KI-Investitionen und echtem unternehmerischem Mehrwert, indem wir ganzheitliche Full-Stack-KI-Lösungen für unsere Kunden entwickeln. Mit unserer fundierten Branchen-, Prozess- und Engineering-Expertise integrieren wir die spezifischen Anforderungen von Unternehmen passgenau in Technologiesysteme. So entfalten wir das menschliche Potenzial, erzielen greifbare Ergebnisse und sichern globalen Unternehmen in einer sich rasant wandelnden Welt den entscheidenden Vorsprung. Erfahren Sie mehr unter cognizant.ai oder @cognizant.
Zusätzliche Informationen zur Beschäftigung
Die Vergütungsinformationen sind zum Zeitpunkt der Veröffentlichung dieser Stellenausschreibung korrekt. Cognizant behält sich das Recht vor, diese Informationen jederzeit unter Beachtung der geltenden gesetzlichen Bestimmungen zu ändern.
Bewerberinnen und Bewerber können verpflichtet sein, an Vorstellungsgesprächen persönlich oder per Videokonferenz teilzunehmen. Darüber hinaus kann es erforderlich sein, bei jedem Gespräch einen gültigen staatlichen Lichtbildausweis vorzulegen.
Cognizant ist ein Arbeitgeber mit Chancengleichheit. Ihre Bewerbung und Kandidatur werden nicht aufgrund von Rasse, Hautfarbe, Geschlecht, Religion, Glaubensbekenntnis, sexueller Orientierung, Geschlechtsidentität, nationaler Herkunft, Behinderung, genetischen Informationen, Schwangerschaft, Veteranenstatus oder sonstiger durch bundes‑, landes‑ oder kommunalrechtliche Vorschriften geschützter Merkmale berücksichtigt oder abgelehnt.
