Lead Data Architect

DHIRA Company Overview 

DHIRA is a leading company specializing in intelligent transformation, where we leverage advanced AI/ML and data-driven solutions to revolutionize business operations. Unlike traditional digital transformation, which focuses on transaction automation, our intelligent transformation encompasses both transactional automation and deep analytics for comprehensive insights. Our expertise in data engineering, data quality, and master data management ensures robust and scalable AI/ML applications. Utilizing cutting-edge technologies across AWS, Azure, GCP, and on-premises Hadoop systems, we deliver efficient and innovative data solutions. Our vision is embodied in the Akashic platform, designed to provide seamless, end-to-end analytics. At DHIRA, we are committed to excellence, driving impactful contributions to the industry. Join us to be part of a dynamic team at the forefront of intelligent transformation

Role- Data Architect – Evolution of Databases, Data Modeling, and Modern Data Practices

Location

Position Overview:

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.

Data Modeling Expertise:

Entity-Relationship Models (ER Models)

–            Transition ER models into OLAP environments with robust dimensional modeling, including star and snowflake schemas.

Vector Database

–            Establish standards for schema design across diverse database systems, focusing on scalability and query performance.

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).

hybrid data architectures

–            Ensure compatibility and performance optimization across these systems for real-time and batch processing.

Data Warehousing and Analytics:

Data Warehouses

–            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:

Vector Databases

–            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.

Strategic Leadership:

data strategy

–            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.

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.

Experience:

6+ years

data models

evolution of databases

Technical Proficiency:

ER modeling

–            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 non-technical audiences.

–            Proven ability to mentor and upskill teams, fostering a collaborative environment.

•            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.