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Cisco

Data Architect, 13+ years exp, Snowflake, dBT, Python

Cisco

Data Architect responsible for defining architecture and standards for Cisco's enterprise data platforms. Leading modernization strategies and collaborating with teams to enhance data-driven decision-making.

Posted 7/23/2026full-timeBangalore • 🇮🇳 IndiaSeniorLeadWebsite

Core Competencies

Role fit
Core Competencies

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Demonstrates expertise in designing and implementing enterprise data architectures, including data lakes, lakehouses, and data warehouses, while ensuring data governance, quality, and compliance across platforms. Proficient in leveraging cloud-native technologies and data integration patterns to optimize data solutions for scalability and performance.

Highest-signal resume keywords
Enterprise Data Architecture DesignCloud Platforms (AWS, GCP, Snowflake)Data Modeling (Conceptual, Logical, Physical)Data Pipeline Orchestration (Airflow, DBT, Informatica)CI/CD and Infrastructure as Code (Terraform, CloudFormation)

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Applicant Tracking System Keywords

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Hard Skills
Data ArchitectureData EngineeringData IntegrationAPI DevelopmentData GovernanceData QualityData ModelingCloud-Native TechnologiesProgramming (Python, SQL, Java)Containerization (Docker, Kubernetes)
Soft Skills
MentoringCollaborationTechnical LeadershipInfluencingKnowledge Sharing
Tools & Technologies
AirflowDBTInformaticaSnowflakeBigQueryDockerKubernetesTerraformCloudFormation
Industry Keywords
Data LakesLakehousesData WarehousesData Governance FrameworksData LineageReal-Time Data AccessCloud-Native PlatformsEnterprise-Scale Systems

Tech Stack

Tools & technologies
AirflowAWSBigQueryCloudDockerGoogle Cloud PlatformInformaticaJavaKubernetesMicroservicesNoSQLPythonSQLTerraform

About the role

Key responsibilities & impact
  • Define and own the enterprise data architecture vision, standards, and multi-year roadmap across ingestion, storage, transformation, discovery, and governance.
  • Design scalable, secure, and highly available data architectures—including data lakes, lakehouses, data warehouses, and real-time streaming systems—leveraging modern cloud-native technologies.
  • Define and lead target-state architecture and migration strategies that modernize legacy data infrastructure onto cloud-native platforms (e.g., GCP, Snowflake).
  • Establish architectural standards, reference patterns, and best practices for data modeling, integration, metadata management, and platform security across teams.
  • Establish enterprise data modeling standards and governance/lineage frameworks that improve data quality, discoverability, and compliance across multiple business domains.
  • Design API- and event-driven integration architectures that enable real-time data access across the enterprise.
  • Partner closely with data engineers, analytics teams, platform engineers, and business leaders to translate strategic priorities into robust, well-governed data solutions.
  • Lead architecture decisions for platform modernization, evaluating and selecting technologies to optimize efficiency, cost, and scale.
  • Define data governance, lineage, quality, and compliance frameworks, ensuring they are embedded across platform design and operations.
  • Provide architectural oversight and design reviews, ensuring solutions meet reliability, scalability, performance, and security requirements.
  • Champion conceptual, logical, and physical data modeling practices that support both operational and analytical workloads.
  • Mentor engineers and architects, fostering a culture of technical excellence, sound design thinking, and knowledge sharing.
  • Act as a key technical advisor to leadership, influencing architecture decisions, technology investments, and strategic direction across the data organization.

Requirements

What you’ll need
  • Bachelor's or Master's degree in Computer Science, Engineering, or related field.
  • 13+ years of professional experience in data architecture, data engineering, or large-scale data platform design.
  • Deep expertise designing enterprise data architectures—data lakes, lakehouses, and data warehouses—at scale.
  • Strong proficiency with cloud platforms (AWS, GCP, or Snowflake) and modern, cloud-native data architectures.
  • Expert-level data modeling skills (conceptual, logical, and physical) across relational, dimensional, and NoSQL paradigms.
  • Proven experience defining data integration, ingestion, and API/microservices patterns for enterprise-scale systems.
  • Strong knowledge of data pipeline orchestration tools (Airflow, DBT, Informatica) and cloud-native storage (Snowflake, BigQuery).
  • Hands-on understanding of CI/CD, containerization (Docker, Kubernetes), and infrastructure as code (Terraform, CloudFormation).
  • Solid programming or scripting skills (Python, SQL, Java, or similar).

Benefits

Comp & perks
  • Health insurance
  • Retirement plans
  • Paid time off
  • Flexible work arrangements
  • Professional development