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.