Data Architect

Data Architect with Bachelor’s Degree in Computer Science, Computer Information Systems, Information Technology, or a combination of education and experience equating to the U.S. equivalent of a Bachelor’s degree in one of the aforementioned subjects.

• Design, define, and maintain enterprise data architecture supporting analytical, operational, and regulatory-driven systems across multiple business domains.
• Lead requirements analysis by working closely with business stakeholders, subject matter experts, QA, and engineering teams to translate business needs into scalable data solutions.
• Develop and maintain conceptual, logical, and physical data models for data warehouses, data marts, operational data stores, and enterprise applications using industry-standard modeling tools.
• Architect and guide ETL and data integration frameworks, including data ingestion, transformation, validation, and error-handling processes across heterogeneous source systems.
• Design and optimize database schemas and structures to support high-volume, high-availability enterprise data platforms.
• Establish and enforce data quality, data governance, and data stewardship standards to ensure accuracy, consistency, and auditability of enterprise data.
• Perform forward and reverse engineering of database designs and conduct impact analysis for schema and data changes.
• Support performance tuning, capacity planning, and optimization of databases and ETL processes to meet scalability and availability requirements.
• Lead or contribute to master data management (MDM) initiatives, including customer, product, vendor, and identity domains.
• Architect data solutions integrating data from multiple enterprise systems, including ERP, legacy platforms, and external data sources.
• Produce and maintain comprehensive technical documentation, including architecture diagrams, data models, mapping documents, design specifications, and validation artifacts.
• Provide technical leadership and mentoring to data engineers, developers, and analysts throughout the data platform lifecycle.
• Participate in testing activities, including unit testing, system testing, performance testing, and volume testing, to ensure system readiness prior to QA and production deployments.
• Collaborate with executive leadership to support technology roadmaps, long-term data strategies, and enterprise architecture initiatives.
• Support production systems by assisting with issue analysis, defect resolution, and continuous improvement of data platforms.

• Minimum of 5 years of progressively responsible professional experience in the field of data architecture, data modeling, and enterprise data management.
• Hands-on experience in enterprise data architecture design, including defining data standards, integration patterns, and scalable data platforms.
• Demonstrated experience in conceptual, logical, and physical data modeling for complex enterprise systems.
• Proven experience designing and supporting ETL and data integration frameworks, including data ingestion, transformation, validation, and error handling.
• Experience working with relational and/or analytical databases, including schema design, performance tuning, and optimization.
• Strong background in data quality, data governance, metadata management, and auditability.
• Experience collaborating with business stakeholders, QA teams, and engineering teams to translate functional requirements into technical data solutions.
• Experience supporting testing activities, including unit testing, system testing, performance testing, and volume testing of data pipelines.
• Ability to produce and maintain technical documentation, such as architecture diagrams, data models, mapping documents, and design specifications.
• Experience working in complex, multi-system enterprise environments, including legacy systems and modern data platforms.

• Enterprise Data Architecture aligned with TOGAF standards, including conceptual, logical, and physical data modeling; master data management (MDM); metadata management; data governance; and enterprise data quality frameworks.
• Big Data and analytics platforms including Hadoop ecosystem technologies (Hive, HDFS, Solr), real-time data streaming using Apache Kafka, and distributed and parallel data warehouse architectures supporting large-scale financial and pension data processing.
• Cloud-based data platforms and hybrid architectures utilizing Amazon Web Services (AWS), Microsoft Azure, Oracle Cloud and Oracle Fusion, including cloud-native data lakes, data warehouses, and data virtualization technologies such as Dremio.
• Relational and analytical database technologies including Oracle (Enterprise Editions), Postgre
  
  SQL, MySQL, Microsoft SQL Server, Teradata, Amazon Redshift, and No
  
  SQL data stores such as Mongo
  
  DB and Cassandra.
• ETL, data integration, and orchestration technologies including Alteryx, Informatica, IBM Data Stage, Microsoft SSIS, Kafka-based…