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# Data Manager II

- **Company:** Booking.com
- **Location:** Amsterdam, Netherlands
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Airflow, Data Analysis, Numbers (Spreadsheet), Continuous Integration, Data Dictionary, Information Engineering, Data Governance, Data Masking, Data Profiling, Data Visualization, Digital Assets, Python (Programming Language), Metadata, Meta-Data Management, MicroStrategy, Scrum Methodology, Cloud Services, Standard Sql, SQL Databases, Tableau (Software), Data Processing, Scripting, Cloud Platform System, Snowflake, Agentic-AI, Git, Data Layers, Collibra, Data Management, Data Pipelines
- **Published:** October 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=98d26cb80e32d6bd

## About the Role

* 3-5 years in data management, data governance, data quality, or a related role (e.g., data analyst or data engineer with a strong DQ/governance focus).
 * Strong SQL experience writing and managing complex logic, along with scripting skills in Python.
 * Proficient in data profiling and exploratory data analysis, with hands-on experience in data visualization tools (e.g., Tableau, Microstrategy).
 * Hands-on experience with data cataloguing, lineage, and DQ platforms (e.g., Collibra, Monte Carlo, or equivalent).
 * Demonstrated experience implementing DQ rules, dimensions, thresholds/SLOs, and metadata/master data management.
 * Experience applying compliance requirements (e.g., GDPR, ethical data handling, responsible AI) to data management initiatives.
 * Strong critical thinking, root-cause analysis capabilities, and excellent communication skills across technical and business disciplines.

Desirable / Nice to Have:

 * Experience working with finance data or within SOx / regulated reporting environments.
 * Experience with cloud data platforms (e.g., Snowflake) including access control, security, and data masking.
 * Familiarity with modern data engineering practices (dbt, Airflow/Dagster, Git, CI/CD) and Agile delivery frameworks (Scrum/Kanban).
 * Experience utilizing AI agents and technologies to improve productivity in governance processes.

## Description

Establish, oversee, and drive adherence to data management practices, controls, and governance standards across the finance data estate.

 * Design, implement, and operate data quality frameworks, observability checks, and root-cause remediation to ensure finance data is trusted and fit for purpose.
 * Act as the vital bridge between business requirements, data engineering platforms, and regulatory compliance.
 * Safeguard SOx-relevant data assets, ensure ethical data handling and responsible AI adoption, and embed governance and quality controls into platform modernization programs.
 * FP&A Tech builds and operates the data platforms and data products that Financial Planning & Analysis depends on, where trusted numbers, governance, risk management, and data quality are fundamental conditions for how Finance functions.
 * You will work extensively with FP&A Analysts, Data Engineers, Product Managers, Data Scientists, Accounting, Tax, Audit, Financial Systems, and Group Reporting teams., Governance, Risk, and Controls:
 * Implement Data & AI Governance standards and playbooks aligned with enterprise frameworks and regulatory/audit requirements.
 * Support the governance of SOx-relevant data assets: control design and documentation, change management, segregation of duties, access control, and audit evidence preparation.
 * Manage data risks and define lifecycle requirements (retention, archival) for key finance assets.

Data Quality Frameworks & Monitoring:

 * Design and operate Data Quality (DQ) dimensions, thresholds/SLOs, rules, and scoring for priority finance assets and metrics.
 * Embed DQ checks and observability into batch and streaming pipelines, shifting quality checks left into development.
 * Build DQ dashboards and lead root-cause analysis on data incidents to drive permanent remediation.

Metadata, Classification & Stewardship:

 * Apply classification, taxonomies, and metadata management to maintain accurate data dictionaries, business glossaries, metric definitions, and lineage catalogues.
 * Establish stewardship across key finance domains and leverage data management APIs/tooling (catalogues, lineage, DQ platforms).

Delivery & Stakeholder Management:

 * Lead data governance and quality initiatives end-to-end while communicating the business impact of data quality to technical and business stakeholders.
 * Embed governance and quality requirements into platform modernization and migration programs (e.g., semantic layers, self-service analytics).

Hands-on Analysis & Community:

 * Use SQL and Python to profile data, validate DQ checks, and lead root-cause investigations.
 * Coach data engineers and analysts on governance/DQ topics and contribute best practices to the wider Data Management community.