Job description

Role & Responsibility: • Designing, developing, implementing, and translating business requirements and the overall organizational data strategy, including standards, principles, data sources, storage, pipelines, data flow, and data security policies. • Design and implement infrastructure components (Clusters, VMs, SQL Server, Vnet, Integrations) to support data migration deliverables on time and on budget. • Collaborating with data engineers, data scientists, and other stakeholders to execute the data strategy. • Communicating and defining data architecture patterns in the organization that guide the data framework. • Support/lead data teams to develop secure, scalable, high-performance, and reliable systems to support data migration, data cleansing, and reporting. • Other activities to support data migration/data cleansing, as needed by the project team. • Transactional and Analytical Data Modeling expertise. • Strong Data Governance and Data Quality principles and experience with incorporating best data quality practices in data pipelines. • Progressive career with on-hands on Data Engineering experience. Required Qualifications • 10+ years of related work experience. • At least 7+ years of experience as a cloud solutions architect or equivalent in the area of data solutions designing cloud-based data architectures, data modeling, data warehousing/data lake solutions and data migration scenarios including both hybrid and pure cloud data solutions. • At least 5+ years of hands on experience with any of the following Azure data services: Azure Synapse, Azure SQL Database, Azure Cosmos DB, Azure Data Lake, Azure Data Factory, and Azure Databricks or related • Experience with Azure cloud solutions: related to data acquisition, transformation, storage, analysis, and loading. This includes SQL application data as well as unstructured data (file attachments, et al). • Databases and operating systems: experienced in various SQL database languages (T-SQL, Databricks, PL/SQL, MySQL). Understands the advantages and drawbacks of each and how they can be set up effectively and securely across different data platforms and environments (development and production). • Data architecture: Recommend best practices on data architecture for enterprise data warehouse development. Possesses a solid understanding of the organization's data infrastructure and how different systems interact. • Data security and governance: Rapidly gather and understand organizational standards, policies, processes, roles, and metrics that ensure the effective and efficient use of data/information. Skilled in data management and governance strategies with a good understanding of the risks and mitigations of each. Ensure data governance is in line with the overall organizational strategy. • Communication and leadership: communicate clear technical solutions to complex data problems to both technical and non-technical audiences.

Requirements