Data Architect - 13+ years exp, Snowflake, dbt, Python

Jobtailor; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Jobtailor is seeking an Senior Enterprise Data Architect in Bengaluru to own the data architecture roadmap, design scalable data systems, and modernize legacy platforms on cloud-native technologies. You will lead governance, data modeling, and integration patterns while mentoring teams.

Ideal candidates have 13+ years in data architecture, deep experience with data lakes, lakehouses, and warehouses, and strong cloud-platform skills (AWS/GCP/Snowflake). This is a full-time on-site Bengaluru role.

Qualifications

• Bachelor's or Master's in Computer Science, Engineering, or related field.
• 13+ years in data architecture, data engineering, or large-scale data platforms.
• Experience designing data lakes, lakehouses, and data warehouses at scale.
• Proficiency with AWS, GCP, or Snowflake cloud platforms.
• Expert-level data modeling (conceptual, logical, physical).
• Experience defining data integration, ingestion, and API/microservices patterns.
• Knowledge of Airflow, DBT, Informatica and cloud-native storage (Snowflake, BigQuery).
• Hands-on CI/CD, containerization (Docker, Kubernetes), and IaC (Terraform, CloudFormation).
• Programming/scripting in Python, SQL, Java, or similar.

Responsibilities

• Define and own enterprise data architecture vision, standards, and multi-year roadmap.
• Design scalable, secure data architectures—lakes, lakehouses, warehouses, and streams.
• Lead migration to cloud-native platforms; modernize legacy infra.
• Establish governance, lineage, quality, and security across teams.
• Create API- and event-driven integration for real-time data access.
• Mentor engineers and architects; advise leadership on tech direction.

Skills

Enterprise data architecture design

Data modeling

Cloud-native technologies

Data integration

APIs & microservices patterns

Data governance

Education

Bachelor's or Master's in CS/Engineering

Tools

Airflow

DBT

Informatica

Snowflake

BigQuery

Docker

Kubernetes

Terraform

CloudFormation

Job description

• 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

• 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).

Core Competencies

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 Design
• Cloud Platforms (AWS, GCP, Snowflake)
• Data Modeling (Conceptual, Logical, Physical)
• Data Pipeline Orchestration (Airflow, DBT, Informatica)
• CI/CD and Infrastructure as Code (Terraform, CloudFormation)

Hard Skills

• Data Architecture
• Data Engineering
• Data Integration
• API Development
• Data Governance
• Data Qualityi>
• Data Modeling
• Cloud-Native Technologies
• Programming (Python, SQL, Java)i>
• Containerization (Docker, Kubernetes)

Soft Skills

• Mentoring
• Collaboration
• Technical Leadership
• Influencing
• Knowledge Sharing

Industry Keywords

• Data Lakes
• Lakehouses
• Data Warehouses
• Data Governance Frameworks
• Data Lineage
• Real-Time Data Access
• Cloud-Native Platforms
• Enterprise-Scale Systems

Tools & Technologies

• Airflow
• DBT
• Informatica
• Snowflake
• BigQuery
• Docker
• Kubernetes
• Terraform
• CloudFormation