Data Manager - Fintech Foundation

About us: At , data drives our decisions. Technology is at our core. And innovation is everywhere. But our company is more than datasets, lines of code or A/B tests. We’re the thrill of the first night in a new place. The excitement of the next morning. The friends you make. The journeys you take. The sights you see. And the food you sample.

Through our products, partners and people, we can empower everyone to experience the world.

's Fin Tech business unit – with teams in Amsterdam, Bangalore, London and Shanghai – develops new products and drives innovation to help us continue removing financial friction from the travel process. That makes buying and selling travel-related products and services simpler for both travelers and partners.

The Data Track’s mission is to govern, acquire, cleanse, enrich, store, and distribute comprehensive and reliable Fintech transactional data. We follow the trail of operational activity, model it to have it represent what it should, then strive to ensure that any data point that anyone needs after the fact is available where they need it, when they need it, and how they need it, without any concerns about accuracy or trustworthiness.

The Data Track contains teams who own data infrastructure as well as data pipelines to serve our stakeholders.

Role Overview
The Data Manager within the Data Track is critical for ensuring the quality, reliability, and usability of all Fintech transactional data. Key tasks and responsibilities include:

• Data Support and Optimization:
  Improve existing support procedures, streamline processes for key stakeholders, collect performance metrics, analyze support trends, and propose strategic solutions to recurring data issues

• Data Quality Champion:
  Act as the primary advisor and champion for data quality initiatives. This involves moving from quality observability and local fixes towards a comprehensive, holistic view of data quality across all our pipelines and data assets

• Data Modeling and Semantics:
  Participate actively in modeling the data within Fintech Products. This requires diving deep into the technical semantics, understanding complex business processes, and ensuring data structures accurately reflect operational reality

Key Job Responsibilities and Duties

• Support the creation of physical data models that can be operationalized within the company’s workflows and infrastructure to ensure that data solutions meet business requirements

• Support the mapping of data flows between systems and workflows across the company to drive efficiency and ensure proper controls

• Support the implementation of efficient data ingestion solutions, batch processing, and event-based streams to enable real-time actions and rapid business decisions

• Connect datasets from different systems into a unified solution to deliver high-value data

• Monitor relevant SLIs and SLOs to support ownership of data solutions end-to-end

• Support the definition of quality requirements for data assets, based on standards for all data quality dimensions, to ensure design of high-quality data solutions

• Support the definition of quality requirements for data assets, based on standards for all data quality dimensions, to ensure design of high-quality data solutions

• Develop data validation solutions for validating values and schemas and use them to ensure data accuracy and reliability

• Implement solutions for monitoring, failure detection, and data availability and use them to ensure data timeliness and completeness

• Build software applications by using the relevant development languages for the systems, services and tools used by the specific business area

• Ensure data timeliness based on business requirements by driving strategy and implementation of monitoring and failure detection

• Expertise and experience in data analysis

• Expertise in data management, data visualization and quality

Role Qualifications and Requirements

• Knowledge of data modelling methods (Relational, Dimensional)

• Intermediate knowledge of data quality requirements and implementation methodologies

• Intermediate knowledge of Data Management requirements based on best practices, e.g. DAMA‑DMBOK

• Intermediate knowledge of one or more database…