Data Analyst
92% skill overlap
Your role
Data Governance Analyst
Full profileYour role
Also known as Data quality specialist · CDO · Chief analytics officer · Chief Data Officerand 7 more
Chief data officers · Data integrity officer · Data integrity specialist · Data quality expert · Data quality officer · Data quality specialists · Head of Data
- L3 salary
- Not published yet
- Upcoming dated changes
- 8 changes ahead
What the role does
A data governance analyst helps an organisation know what data it has, who owns it, how good it is and where it comes from. The role maintains data catalogues and business glossaries, defines and monitors data quality rules, documents lineage from source systems to reports, supports data owners and stewards, and tracks data issues to resolution. In finance it supports rules on risk data aggregation such as BCBS 239 (Basel Committee principles for risk data aggregation and reporting) and data protection under the GDPR (General Data Protection Regulation).
Read a day in the life
A day in the life
The day often starts with the data quality dashboard. The analyst looks at rules that failed overnight, for example customer records without a country code or trades with missing counterparty identifiers, and checks whether each failure is new or already logged. New issues are raised with the data owner, with an impact note and a proposed fix. Mid-morning there may be a working session with a finance or risk team to define terms in the business glossary: what exactly counts as an active customer, or which date defines a default. The analyst records the agreed definition, links it to the physical data fields and to the reports that use it. In the afternoon the work may move to lineage, tracing how a figure in a regulatory report is built from source systems, or to preparing evidence for an internal audit on data controls. The day ends with updates to the issue log and the governance committee pack.
Levels
- L1 Starting out
- L2 Independent
- L3 Senior or specialist
- L4 Leads a team or an area
- L5 Leads the function
Full level descriptions and job titles
The EFO level names are the common scale; job titles differ between employers.
| Level | What the work looks like | Typical years | Typical job titles |
|---|---|---|---|
| L1 Starting out | Maintains catalogue and glossary entries, runs data quality checks, logs issues and learns data governance methods, the main data domains and the relevant rules under supervision. | 0-2 | Banks: Analyst, Junior Analyst, Associate (asset servicing), Trainee, GraduateAsset servicers: Junior, Specialist (entry), Officer |
| L2 Independent | Owns governance activities for a data domain, writes quality rules and definitions with business users, documents lineage and trains data stewards on tools and processes. | 2-5 | Banks: Analyst / Senior Analyst, AssociateAsset servicers: Specialist, Senior Specialist, Senior Officer |
| L3 Senior or specialist | Designs data quality and lineage frameworks for complex domains, handles cross-team issues at the root, supports audits and regulatory reviews and represents governance in data projects. | 5-9 | Banks: Senior Associate, AVP, Assistant ManagerAsset servicers: Senior Specialist, Expert, Team Lead (small team) |
| L4 Leads a team or an area | Leads a data governance team, owns policies, standards and committee reporting, manages relations with data owners and is the escalation point for material data issues. | 8-14 | Banks: VP, Manager, Senior ManagerAsset servicers: Team Lead, Manager, Head of Section |
| L5 Leads the function | Accountable for data governance in a legal entity or region, often reporting to the chief data officer, sets the data strategy and policies and reports data risk to senior management and boards. | 12+ | Banks: Director, Executive Director, Managing Director, Head of functionAsset servicers: Head of Department, Director |
Skills
Where the skill weights come from
Each role lists its skills with a tier and a weight. The weights of one role add up to 100 percent.
Source: the EFO role catalogue, built from the ESCO classification and reviewed by the editors.
The percentage is the skill's share of the role; the bars are drawn relative to the anchor skill, which is shown full.
Critical
- Querying data with SQLWeight 12.2%Relevant to you
- Descriptive statistics and distributionsWeight 8.2%Relevant to you
- Visualisation tools (Power BI, Tableau)Weight 8.2%Relevant to you
- Data cleaning and validationWeight 8.2%Relevant to you
- Advanced Microsoft ExcelWeight 8.2%Relevant to you
- Data governance, quality and lineageWeight 8.2%Relevant to you
Important and supporting skills 17
Important
- Communicating data to non-technical audiencesWeight 4.1%Relevant to you
- Choosing the right metric for the questionWeight 4.1%Relevant to you
- Hypothesis testing and significanceWeight 4.1%Relevant to you
- Python for data analysisWeight 4.1%Relevant to you
- Defining and using KPIsWeight 4.1%Relevant to you
- Professional written communicationWeight 4.1%Relevant to you
- Structuring ambiguous problemsWeight 4.1%Relevant to you
- Stakeholder managementWeight 4.1%Relevant to you
Supporting
- Experiment design and A/B testingWeight 2.0%Relevant to you
- Data modelling and schema designWeight 2.0%Relevant to you
- Forecasting methods and time seriesWeight 2.0%Relevant to you
- Attention to detailWeight 2.0%Relevant to you
- Assessing the quality of sourcesWeight 2.0%Relevant to you
- Data platforms (Snowflake, BigQuery)Weight 2.0%Relevant to you
Useful
- Git version controlWeight 0.7%Relevant to you
- ETL/ELT pipeline designWeight 0.7%Relevant to you
- Intellectual curiosityWeight 0.7%Relevant to you
Processes
The work the role takes part in.
What each process covers
- Data management and governanceMain
Where the role concentrates
How AI is changing the role
Machine learning tools now profile data, suggest quality rules, classify sensitive fields and propose lineage automatically, and generative AI can draft glossary definitions.
Read the full assessment
Machine learning tools now profile data, suggest quality rules, classify sensitive fields and propose lineage automatically, and generative AI can draft glossary definitions. The European Banking Authority stresses that AI in banks depends on sound data governance and quality, and the EU AI Act sets data requirements for high-risk systems. The World Economic Forum Future of Jobs Report 2025 lists AI and big data among the fastest-growing skills. Value moves to agreeing definitions between people, assigning ownership, judging which issues matter, and governing the data used to train and run AI models.
Dated changes for this role
- TARGET Services November 2026 releases moved to 28 November 2026Relevant to you
- DORA register of information: 2027 annual submissionRelevant to you
- Swift defers SR2026 and the end of unstructured addressesRelevant to you
- CSRD scope narrowed by Omnibus I; second wave reports from financial year 2027Relevant to you
- Pay Transparency Directive: first gender pay gap reportsRelevant to you
Salary snapshot
Salary figures are not published yet
The L3 salary range for this role, with its sources, will appear here when the salaries section opens.
Certifications
- Microsoft Power BI Data Analyst PL-300
- Tableau Desktop Specialist
- SnowPro Core
- dbt Analytics Engineering
Concepts to know
Neighbouring roles
Roles that need many of the same skills, by weighted skill overlap, with the skills you would need to add.
Data Analyst (Banking)
92% skill overlap
To add: Trade lifecycle
Your role
Business Intelligence Analyst
62% skill overlap
To add: Process mapping and redesign
Your role
BI Analyst (Banking)
60% skill overlap
To add: Trade lifecycle, Process mapping and redesign
Your role
Data Scientist
56% skill overlap
To add: Machine learning fundamentals, Causal inference and econometrics
Your role
Data Scientist (Banking)
55% skill overlap
To add: Machine learning fundamentals, Credit risk models (IFRS 9, IRB), Causal inference and econometrics
Your role
Do this job? Suggest a correction
Sources
- ESCO occupation: data quality specialist (external site)
- ESCO occupation: chief data officer (external site)
- Basel Committee on Banking Supervision, Principles for effective risk data aggregation and risk reporting (external site)
- DAMA International, data management body of knowledge (external site)
- World Economic Forum, Future of Jobs Report 2025 (external site)
Data governanceData governance is the set of roles, policies, standards and forums that decide how data is defined, owned, protected and kept fit for use. It assigns accountability (data owners and stewards), sets quality thresholds and resolves disputes on definitions.
BCBS 239 (risk data aggregation)BCBS 239 is the Basel Committee's 2013 set of principles for effective risk data aggregation and risk reporting. It asks banks to have governance, data architecture and reporting that produce accurate, complete and timely risk data, also under stress. The ECB treats it as a supervisory priority.
Data ownerA data owner is the senior business person accountable for a defined set of data: its definition, quality standards, access rules and fitness for use. The owner approves changes and remediation priorities, while data stewards do the day-to-day work.
Internal auditInternal audit is the independent function that gives the board assurance on governance, risk management and internal controls. In the three lines model it is the third line, reporting to the audit committee. Banking rules require it to be independent of the activities it audits.
Generative AIGenerative AI is a family of AI models that produce new content (text, code, images, audio) in response to a prompt, by learning patterns from very large datasets. In finance it is used for drafting, summarising documents, coding help and customer service assistants.
AI Act (Artificial Intelligence Act)The AI Act, Regulation (EU) 2024/1689, is the EU law that sets rules for AI systems according to their risk: some practices are banned, high-risk systems face strict requirements, and general-purpose AI models have transparency duties. Credit scoring of individuals is a high-risk use.