Your degree in Data Science, Data Engineering Computer Science, Information Technology or related as well as a minimum of 4 years proven experience in data engineering or related field will enable you to: Competencies & Skills: Strong technical expertise in data engineering concepts, tools, and technologies, with hands-on experience in data pipeline development, ETL, and data integration. Strategic thinker with a track record of driving business impact through data driven initiatives and solutions. Effective communication and stakeholder management skills, with the ability to influence and collaborate with senior leadership and cross-functional teams. Experience in defining and executing data strategies, roadmaps, and initiatives in alignment with business goals and objectives. Deep understanding of data governance, compliance, and regulatory requirements, with a focus on data security and privacy. Duties: Execute Data Strategy: Implement the company's data strategy, encompassing data architecture, governance, analytics, and data-driven decision-making. Define Best Practices and Standards: Implement best practices, standards, and methodologies for data engineering, ensuring adherence to industry standards and compliance requirements as defined by management. Establish guidelines for data acquisition, integration, storage, and analysis to ensure consistency, reliability, and scalability. Collaborate with Cross-Functional Teams: Collaborate with cross-functional teams to understand business needs and drive data-driven solutions that address specific challenges and opportunities. Partner with business leaders to identify opportunities for leveraging data assets and driving business value through analytics and insights. Responsibilities: Evaluate Emerging Technologies: Evaluate and recommend emerging technologies, tools, and platforms to enhance data infrastructure, scalability, and performance. Stay abreast of industry trends and developments in data engineering and analytics and assess their potential impact on the organization's data strategy and capabilities. Establish External Partnerships: Establish and maintain relationships with external partners, vendors, and stakeholders to support data-related initiatives and partnerships. Collaborate with external partners to co-develop and integrate data related solutions that meet business needs and drive innovation. Drive Data Quality and Governance: Drive initiatives for data quality, governance, and security, ensuring data integrity, privacy, and compliance with regulatory requirements. Implement controls and safeguards to mitigate risks and protect sensitive information, maintaining the trust and confidence of customers, partners, and stakeholders. Lead Budgeting and Resource Allocation: Lead budgeting and resource allocation for data engineering projects and initiatives, ensuring alignment with strategic priorities and maximizing return on investment. Manage resources effectively to ensure timely delivery of data solutions and initiatives within budget constraints. Innovation in Data Analytics: Coordinate innovation in data analytics, machine learning, and artificial intelligence to unlock insights and drive business value. Champion experimentation and exploration of new tools, methodologies, and best practices to drive continuous improvement in data analytics capabilities.
