Data Architect

Role & responsibilities

• Design and lead enterprise data architectures that are scalable, secure, reliable, and aligned with business objectives.
• Define data architecture strategies, standards, principles, and technology roadmaps.
• Translate business requirements into data models, data platforms, integration architectures, and technical solutions.
• Design and implement data warehouses, data lakes, lakehouses, and modern cloud-based data platforms.
• Develop logical, physical, conceptual, and dimensional data models.
• Design batch and real-time data pipelines using ETL/ELT and streaming technologies.
• Architect data integration solutions across databases, applications, APIs, cloud platforms, and third-party systems.
• Establish data governance, data quality, metadata management, data lineage, and master data management practices.
• Design secure data architectures incorporating access controls, encryption, privacy, compliance, and data-protection standards.
• Optimize data platforms for performance, scalability, availability, reliability, and cost efficiency.
• Lead data migration, modernization, and cloud transformation initiatives.
• Evaluate and recommend data technologies, tools, platforms, and architectural patterns.
• Collaborate with data engineers, software engineers, BI teams, data scientists, cloud teams, security, and business stakeholders.
• Conduct architecture reviews, data-model reviews, technology assessments, and performance evaluations.
• Establish standards for data integration, storage, processing, retention, and lifecycle management.
• Ensure data platforms support analytics, reporting, machine learning, and real-time business requirements.
• Maintain data architecture documentation, data models, lineage diagrams, technical specifications, and standards.
• Mentor data engineers and architects and provide technical leadership across data initiatives.

Preferred candidate profile

• 5 to 10 years of experience in data engineering, database development, data architecture, BI, or enterprise data management.
• Proven experience designing and implementing enterprise-scale data architectures and platforms.
• Strong expertise in data modeling, dimensional modeling, data warehousing, data lakes, and lakehouse architectures.
• Advanced SQL skills and strong experience with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Experience with NoSQL databases such as MongoDB, Cassandra, DynamoDB, or similar technologies.
• Hands-on experience with cloud data platforms such as AWS, Azure, or GCP.
• Experience with modern data platforms and technologies such as Snowflake, Databricks, BigQuery, Redshift, or Azure Synapse.
• Strong knowledge of ETL/ELT, data integration, batch processing, and real-time/streaming architectures.
• Experience with Apache Spark, Kafka, Airflow, dbt, or similar data engineering technologies.
• Strong understanding of data governance, data quality, metadata, data lineage, master data management, and data security.
• Experience designing secure and compliant data architectures with appropriate access controls, encryption, and privacy mechanisms.
• Knowledge of BI and analytics platforms such as Power BI, Tableau, or equivalent tools.
• Experience leading data migration, modernization, cloud adoption, and large-scale data transformation projects.
• Strong understanding of distributed systems, scalability, performance optimization, and cloud cost management.
• Strong analytical, problem-solving, communication, documentation, and stakeholder-management skills.
• Ability to provide technical leadership and mentor data engineers and development teams.
• Bachelor's degree in Computer Science, Information Technology, Data Science, Computer Engineering, Statistics, Mathematics, or a related field; Master's degree is an advantage.
• Relevant certifications such as AWS Data Engineer, Google Professional Data Engineer, Microsoft Azure Data Engineer, Databricks, Snowflake, or DAMA CDMP are preferred.