Data Analyst (Banking)
92% skill overlap
To add: Trade lifecycle
Your role
Data Analyst
Full profileYour role
Also known as Data storage analyst · Data warehouse analyst · Data warehousing analyst
- L3 salary
- Not published yet
- Upcoming dated changes
- 8 changes ahead
What the role does
A data analyst collects, cleans and analyses data to answer business questions and support decisions. The role writes SQL (Structured Query Language) queries, uses spreadsheets and tools such as Power BI, Tableau or Python, applies descriptive statistics, builds reports and dashboards and explains the findings to non-technical colleagues. It works across sectors, including finance, where analysts support operations, finance, risk, marketing and product teams.
Read a day in the life
A day in the life
The day often starts with checking scheduled reports and data loads. If a figure jumps or a table is empty, the analyst looks for the cause before users notice. Then comes the main analysis task, for example why customer churn rose last quarter or which processes cause the most errors. The analyst agrees the question and the definitions with the requester, finds the right tables, writes and tests queries, and cleans the data: duplicates, missing values, inconsistent codes. Simple statistics come next: distributions, trends, comparisons between groups. Around midday there may be a stand-up with the data team or a meeting with an engineer about a new data source. In the afternoon the analyst builds charts that make the result clear and writes a short summary with caveats. The day may end with a presentation to stakeholders, answering their questions and noting follow-up requests in the team's backlog.
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 | Runs and maintains standard reports, writes simple queries, cleans data and learns the business, the data sources and analysis tools under supervision. | 0-2 | Banks: Analyst, Junior Analyst, Associate (asset servicing), Trainee, GraduateAsset servicers: Junior, Specialist (entry), Officer |
| L2 Independent | Owns analyses and dashboards for a business area end to end, agrees definitions with users, presents findings and trains new analysts on tools and data. | 2-5 | Banks: Analyst / Senior Analyst, AssociateAsset servicers: Specialist, Senior Specialist, Senior Officer |
| L3 Senior or specialist | Handles complex or ambiguous questions, designs metrics and data models, applies more advanced statistics and represents analytics in projects and data initiatives. | 5-9 | Banks: Senior Associate, AVP, Assistant ManagerAsset servicers: Senior Specialist, Expert, Team Lead (small team) |
| L4 Leads a team or an area | Leads an analytics team, owns priorities, quality standards and stakeholder relations, and is the escalation point for data issues and conflicting requests. | 8-14 | Banks: VP, Manager, Senior ManagerAsset servicers: Team Lead, Manager, Head of Section |
| L5 Leads the function | Accountable for data analytics in a business unit or region, sets the analytics strategy, tooling and standards, and reports on data value to senior management. | 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 13.1%Relevant to you
- Descriptive statistics and distributionsWeight 8.7%Relevant to you
- Visualisation tools (Power BI, Tableau)Weight 8.7%Relevant to you
- Data cleaning and validationWeight 8.7%Relevant to you
- Advanced Microsoft ExcelWeight 8.7%Relevant to you
Important and supporting skills 18
Important
- Communicating data to non-technical audiencesWeight 4.4%Relevant to you
- Choosing the right metric for the questionWeight 4.4%Relevant to you
- Hypothesis testing and significanceWeight 4.4%Relevant to you
- Python for data analysisWeight 4.4%Relevant to you
- Defining and using KPIsWeight 4.4%Relevant to you
- Professional written communicationWeight 4.4%Relevant to you
- Structuring ambiguous problemsWeight 4.4%Relevant to you
- Stakeholder managementWeight 4.4%Relevant to you
Supporting
- Experiment design and A/B testingWeight 2.2%Relevant to you
- Data modelling and schema designWeight 2.2%Relevant to you
- Forecasting methods and time seriesWeight 2.2%Relevant to you
- Attention to detailWeight 2.2%Relevant to you
- Assessing the quality of sourcesWeight 2.2%Relevant to you
- Data platforms (Snowflake, BigQuery)Weight 2.2%Relevant to you
- Applied generative AI in office work (prompting, evaluation, governance)Weight 2.2%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
- AI and automationSupporting
Where the role concentrates
How AI is changing the role
Generative AI now writes much routine SQL and Python, and analytics tools answer simple questions in natural language, so basic report building takes less time.
Read the full assessment
Generative AI now writes much routine SQL and Python, and analytics tools answer simple questions in natural language, so basic report building takes less time. The World Economic Forum Future of Jobs Report 2025 lists big data specialists and AI and machine learning specialists among the fastest-growing jobs and AI and big data as the fastest-growing skill. Value moves to framing the right question, judging data quality, checking machine-generated code and results, and telling a clear story that leads to a decision. Analysts who add domain knowledge, such as finance, and data governance skills are best placed.
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.
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Do this job? Suggest a correction
Sources
SQL (Structured Query Language)SQL is the standard language for defining, querying and changing data in relational databases, standardised as ISO/IEC 9075. Analysts use it to select, filter, join and aggregate tables; it is the most requested technical skill in finance data roles.
Business intelligence (BI)Business intelligence is the set of tools and practices that turn data into dashboards and reports for decisions, such as sales, cost, risk or service metrics. Typical tools are Power BI and Tableau, fed by a data warehouse.
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.
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.