Technical Data Architect

Job requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.

• Relevant certifications desirable (e.g., AWS Certified Data Analytics, Google Professional Data Engineer).

• Strong analytical and problem-solving skills with the ability to design for scale and reliability.

• Excellent communication skills and the ability to articulate complex architectures to both technical and non technical audiences.

• Ability to work collaboratively in cross-functional teams while providing strong technical leadership.

Job responsibilities

• Develop enterprise wide data architecture strategies aligned with business and technical objectives.

• Define and maintain conceptual, logical, and physical data models across multiple domains.

• Create data flow diagrams, integration patterns, and canonical models to support consistent, scalable data delivery.

• Design, build, and optimise data platforms leveraging AWS, GCP, and hybrid cloud environments.

• Implement real time and batch processing pipelines using modern data engineering frameworks and tools.

• Evaluate new technologies and recommend solutions that enhance performance, scalability, and cost efficiency.

• Define and enforce data governance standards, including metadata management, data lineage, and data quality.

• Ensure compliance with regulatory and industry frameworks (e.g., GDPR, HIPAA).

• Implement encryption, key management, and security best practices across all data platforms.

• Partner with engineering, analytics, product, and business stakeholders to translate requirements into targeted data solutions.

• Provide architectural guidance, technical leadership, and mentorship to data engineering and development teams.

• Support solution design reviews and ensure architectural consistency across initiatives.

• Strong expertise in data modeling, database design, and architecting conceptual/logical/physical models.

• Proven experience with SQL and NoSQL databases (PostgreSQL, MySQL, DynamoDB, Bigtable).

• Hands on experience building ETL/ELT pipelines and optimising data transformations.

• Practical knowledge of AWS (Kinesis, S3, Redshift) and GCP (Pub/Sub, BigQuery).

• Experience with streaming technologies such as Kafka, Confluent, and real time processing engines (Spark, Flink).