Data Architect/Lead

Job description
About the Role:
We are seeking a
Data Architect
with
12+ years of experience
who has a comprehensive understanding of the
evolution of databases , from OLTP to OLAP, and relational systems to NoSQL, Graph, and emerging
Vector Databases . This role requires deep expertise in
data modeling , from traditional ER modeling to advanced dimensional, graph, and vector schemas, along with a strong grasp of the history, best practices, and future trends in data management.
The ideal candidate will bring both historical context and cutting-edge expertise to architect scalable, high-performance data solutions, driving innovation while maintaining strong governance and best practices. This is a leadership role that demands a balance of technical excellence, strategic vision, and team mentorship.
Key Responsibilities:
1.
Data Modeling Expertise:
Design and implement
Entity-Relationship Models (ER Models)
for OLTP systems, ensuring normalization and consistency.
Transition ER models into OLAP environments with robust dimensional modeling, including star and snowflake schemas.
Develop hybrid data models that integrate relational, NoSQL, Graph, and
Vector Database
schemas.
Establish standards for schema design across diverse database systems, focusing on scalability and query performance.
2.
Database Architecture Evolution:
Architect solutions across the database spectrum:
•
Relational databases (PostgreSQL, Oracle, MySQL)
•
NoSQL databases (MongoDB, Cassandra, DynamoDB)
•
Graph databases (Neo4j, Amazon Neptune)
•
Vector databases (Pinecone, Weaviate, Milvus).
Implement
hybrid data architectures
combining OLTP, OLAP, NoSQL, Graph, and Vector systems for diverse business needs.
Ensure compatibility and performance optimization across these systems for real-time and batch processing.
3. Data Warehousing and Analytics:
Lead the development of enterprise-scale
Data Warehouses
capable of supporting advanced analytics and business intelligence.
Design high-performance ETL/ELT pipelines to handle structured and unstructured data with minimal latency.
Optimize OLAP systems for petabyte-scale data storage and low-latency querying.
Emerging Database Technologies:
Drive adoption of
Vector Databases
for AI/ML applications, enabling semantic search and embedding-based queries.
Explore cutting-edge technologies in data lakes, lakehouses, and real-time processing systems.
Evaluate and integrate modern database paradigms, ensuring scalability for future business requirements.
5.
Strategic Leadership:
Define the organization’s
data strategy , aligning with long-term goals and emerging trends.
Collaborate with business and technical stakeholders to design systems that balance transactional and analytical workloads.
Lead efforts in data governance, ensuring compliance with security and privacy regulations.
6.
Mentorship and Innovation:
Mentor junior architects and engineers, fostering a culture of learning and technical excellence.
Promote innovation by introducing best practices, emerging tools, and modern methodologies in data architecture.
Act as a thought leader in database evolution, presenting insights to internal teams and external forums.
Required skills and Qualifications:
•
Experience:
12+ years
of experience in data architecture, with demonstrated expertise across OLTP, OLAP, NoSQL, Graph, and Vector databases.
Proven experience designing and implementing
data models
across relational, NoSQL, graph, and vector systems.
A strong understanding of the
evolution of databases
and their impact on modern data architectures.
•
Technical Proficiency:
Deep expertise in
ER modeling , dimensional modeling, and schema design for modern database systems.
Proficient in SQL and query optimization for relational and analytical databases.
Hands-on experience with NoSQL databases like MongoDB, Cassandra, or DynamoDB.
Strong knowledge of Graph databases (Neo4j, Amazon Neptune) and Vector databases (Pinecone, Milvus, or Weaviate).
Familiarity with modern cloud-based DW platforms (e.g., Snowflake, BigQuery, Redshift) and lakehouse solutions.
•
Knowledge of Data Practices:
Historical and practical understanding of data practices, from schema-on-write to schema-on-read approaches.
Experience in implementing real-time and batch processing systems for diverse workloads.
Strong grasp of data lifecycle management, governance, and security practices.
•
Leadership and Communication:
Ability to lead large-scale data initiatives, balancing technical depth and strategic alignment.
Excellent communication skills to articulate complex ideas to technical and nontechnical audiences.
Proven ability to mentor and upskill teams, fostering a collaborative environment.
Preferred Skills:
• Experience integrating Vector Databases into existing architectures for AI/ML workloads.
• Knowledge of real-time streaming systems (Kafka, Pulsar) and their integration with modern databases.
• Certifications in data-related technologies (e.g., AWS, GCP, Snowflake, Neo4j).
• Hands-on experience with BI tools (e.g., Tableau, Power BI) and AI/ML platforms.