Deskripsi pekerjaan
Responsibilities
- Data Architecture and Engineering:
- Maintaining existing design and ensuring the reliability of entire company pipelines on track with SLA. It is possible to redesign the architecture based on the results of the evaluation.
- Optimize data storage solutions, including BigQuery, Dataflow, and other cloud-native services, to ensure efficient and secure handling of large datasets.
- Business Collaboration and Strategy:
- Work closely with business stakeholders to gather requirements, translate business needs into technical solutions, and ensure that data infrastructure supports key business objectives.
- Partner with product teams, marketing, finance, and other departments to drive data-driven decision-making across the bank.
- Data Governance and Security:
- Ensure the data infrastructure complies with regulatory requirements and internal security standards, with a particular focus on data integrity, privacy, and governance.
- Establish best practices for data quality, consistency, and accessibility across the organization.
- Technical Leadership and Mentorship:
- Provide guidance to the data engineering team, ensuring adherence to best practices in cloud architecture, data modeling, and pipeline development.
- Mentor junior engineers and foster a culture of continuous learning and innovation within the team.
- Performance Optimization and Innovation:
- Continuously monitor and improve data processing performance, scalability, and cost-effectiveness.
- Stay abreast of emerging trends and technologies in data engineering, recommending tools and practices that align with the bank's evolving needs.
- Documentation and Reporting:
- Maintain detailed documentation of data pipelines, workflows, and architecture to ensure transparency and audit-readiness.
- Produce regular reports on data platform performance and provide insights on how data solutions are driving business outcomes.
Requirement
- Bachelor鈥檚 degree in Computer Science, Information Technology, Data Science, or a related field.
- Having 5-8 years of experience in data engineering, with a proven track record of leading data engineering projects on Google Cloud.
- At least 2 years of experience in team management is required.
- Strong technical expertise in Google Cloud services (BigQuery, Dataflow, Pub/Sub, etc.) and data processing frameworks.
- Experience in data modeling, ETL/ELT processes, and building real-time and batch data pipelines.
- Demonstrated ability to work with business stakeholders, translating technical language into business insights and ensuring data solutions support business needs.
- Knowledge of the banking or financial services industry is highly desirable.
- Proficiency in SQL, Python, or other relevant programming languages.
- Strong understanding of data governance, privacy standards (e.g., GDPR), and compliance frameworks.
- Excellent communication skills and ability to collaborate effectively across departments.
