Ekloud - Data Architect

Description

About the Role :

We are seeking a highly experienced and technically adept Data Architect to design and deliver enterprise-grade data architecture solutions that power analytics, AI, and business intelligence initiatives. The ideal candidate will possess deep expertise in data modeling, cloud-native data platforms (Azure/AWS), and data governance frameworks, with a strong focus on building scalable, secure, and high-performance data ecosystems. This role demands a hands-on architect with a passion for modern data technologies, distributed processing, and end-to-end data lifecycle management.

Key Responsibilities

Enterprise Data Architecture Design :

• Architect and implement large-scale data ecosystems that enable analytics, AI, and ML-driven decision-making across the organization.
• Define and enforce data architecture standards, design patterns, and best practices to ensure consistency and scalability.
  
  

Data Modeling

• Develop and maintain conceptual, logical, and physical data models aligning with enterprise data strategy.
• Ensure that data structures are optimized for performance, quality, and compliance.
  
  

Data Platform Engineering

• Design and manage data lakes, data warehouses, and lakehouse environments using Azure Synapse, Databricks, AWS Redshift, or Snowflake.
• Architect and optimize ETL/ELT pipelines using tools such as Azure Data Factory, AWS Glue, or custom solutions with Python/Spark.
  
  

Data Governance & Compliance

• Establish and maintain data governance frameworks, metadata management, and data lineage tracking.
• Ensure compliance with data privacy and regulatory frameworks such as GDPR, HIPAA, and SOC 2.
• Collaborate with InfoSec teams to implement data security, access control, and encryption standards.
  
  

Cloud & Modern Data Stack Integration

• Design cloud-native architectures leveraging scalable storage, compute, and orchestration solutions.
• Integrate modern data stack components such as dbt, Airflow, Kafka, Power BI, Tableau, and APIs for seamless data flow and analytics enablement.
  
  

Performance Optimization & Automation

• Leverage Python and Apache Spark for distributed data processing, automation, and performance tuning.
• Implement CI/CD pipelines for data integration and deploy data quality monitoring frameworks.
  
  

Collaboration & Leadership

• Partner with data engineers, AI/ML engineers, business analysts, and product teams to align data initiatives with organizational goals.
• Mentor technical teams and drive adoption of data-driven design principles and architectural best practices.
  
  

Required Skills & Qualifications

Technical Expertise :

• Strong proficiency in SQL, Python, and Apache Spark for large-scale data engineering.
• Hands-on experience with Azure Synapse, Databricks, AWS Redshift, or Snowflake.
• Solid understanding of ETL/ELT processes, data warehousing, and data lake architectures.
• Experience in designing scalable, event-driven data architectures and implementing real-time data streaming (Kafka, Kinesis, or Event Hubs).
  
  

Architectural & Strategic Skills

• Deep understanding of data modeling (3NF, star schema, data vault) and data lifecycle management.
• Proven experience architecting data platforms on cloud ecosystems (Azure or AWS).
• Familiarity with data mesh and lakehouse paradigms and modern data fabric design.
  
  

Governance & Compliance

• Knowledge of data governance, master data management (MDM), and metadata management tools.
• Experience implementing data lineage and observability frameworks.
• Awareness of regulatory compliance (GDPR, HIPAA, PCI DSS).
  
  

Soft Skills

• Strong analytical, problem-solving, and communication skills.
• Proven ability to collaborate with business and technical stakeholders.
• Leadership experience in driving architecture reviews and mentoring data teams.
  
  

Preferred Qualifications

• Certification in Azure Data Engineer Associate, AWS Data Analytics Specialty, or Snowflake Data Architect.
• Experience with AI/ML model data pipelines and data product architecture.
• Exposure to Kubernetes, Terraform, and CI/CD frameworks for data infrastructure automation.
  
  

(ref:hirist.tech)