At VPS, we help individuals and institutions grow and manage their wealth through data-driven insights and innovative financial solutions. As part of our ongoing digital transformation, we’re building a modern data platform to unlock the full potential of data across the organization. To accelerate this journey, we are looking for skilled
Data Engineers
who are passionate about data, technology, and creating business value.
JOB DESCRIPTION
As an
Data Engineer
at VPS, you will play a critical role at the intersection of business, analytics, and engineering. You will work closely with data analysts, business stakeholders, and IT teams to design and deliver reliable data pipelines, high-quality data models that support decision-making across the organization.
This role sits within the Product Data team and reports to the
Head of Product Data
, under the broader
Product Department
.
Responsibilities
Collaborate with IT teams to ensure scalable and reliable data infrastructure and pipelines.
Design, build, and maintain robust, reusable data models that serve as the foundation for reporting, dashboards, and advanced analytics.
Develop and maintain data pipelines using modern data tools and frameworks (e.g., dbt, Spark, Airflow).
Contribute to the enterprise data catalog to ensure a single source of truth and promote data discoverability.
Promote and apply DataOps best practices in development workflows, including version control, testing, and CI/CD.
Write clean, maintainable SQL and code following internal best practices.
Monitor data quality and performance; troubleshoot and resolve issues as needed.
Continuously improve models based on feedback and evolving business needs.
Qualifications
Required:
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
3+ years of experience in a data-related role (Analytics Engineer, Data Engineer, etc.).
Proficiency in writing optimized SQL queries for data transformation and analysis.
Familiarity with modern data stack tools (e.g., dbt, Airflow, Trino/Presto, Open Metadata).
Experience with big data technologies (e.g., Spark, Hadoop, S3).
Understanding of containerization and orchestration (Docker, K8s, OCP).
Strong problem-solving skills and a proactive, solution-oriented mindset.
Preferred:
Experience in the financial services or wealth management industry.
Proficiency in Git and command-line tools.
Understanding of data lake/lakehouse architectures.
Experience with ML pipelines and Spark development.