Data Architect

• 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., * 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), PostgreSQL, MySQL, Microsoft SQL Server, Teradata, Amazon Redshift, and NoSQL data stores such as MongoDB and Cassandra.
• ETL, data integration, and orchestration technologies including Alteryx, Informatica, IBM DataStage, Microsoft SSIS, Kafka-based streaming pipelines, batch and incremental data processing frameworks, and workflow orchestration using Camunda, Autosys, and UC4.
• DevOps, automation, and containerization technologies including Docker, CI/CD pipelines, Jenkins, Git and Bitbucket, Maven, and automated deployment and monitoring frameworks.
• Business intelligence, reporting, and advanced analytics tools including Tableau, SAS, Cognos, Business Objects, OBIEE, SSRS, and enterprise-level dashboarding solutions.
• Programming and scripting languages including Java, Python, Scala, SQL/PL-SQL, and UNIX/Linux shell scripting.
• Enterprise security and compliance technologies including Kerberos authentication, role-based access control (RBAC), encryption of data at rest and in transit, and secure enterprise data access models.
• AI-assisted architecture, design, and productivity tools including ChatGPT, Claude, Grok, and related generative AI platforms for solution design, documentation, and analysis.

Do you have experience in Unit testing?, Do you have a Bachelor’s degree?, 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., * 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.