Data Analyst
92% skill overlap
Your role
Data Analyst (Banking)
Full profileYour role
- L3 salary
- Not published yet
- Upcoming dated changes
- 6 changes ahead
What the role does
A data analyst in banking analyses data on trades, clients, payments, operations and risk to answer business questions and improve processes. The role writes SQL (Structured Query Language) queries on bank data warehouses, cleans and validates data, applies descriptive statistics and builds reports in Excel, Power BI or Tableau. It needs knowledge of banking products and the trade lifecycle, from capture through confirmation to settlement, so that data is read correctly.
Read a day in the life
A day in the life
The morning begins with checks on overnight data loads and on the daily operational reports that teams rely on. A typical first request might come from an operations manager: why have settlement fails in one market increased this month. The analyst pulls trade and settlement data, joins it with counterparty and instrument reference data, and cleans duplicates and missing fields. Grouping the fails by counterparty, instrument type and reason code shows that most come from a few counterparties with missing settlement instructions. The analyst checks the result with the operations team, who know the process detail. Around midday there may be a meeting with data engineers on a new source system. In the afternoon the analyst updates a dashboard, documents the definitions used and prepares a short note with findings and suggested actions. Data protection rules and access controls on client data shape how every piece of work is done.
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 standard reports on trade, client and operations data, writes simple queries, checks data quality and learns banking products, the trade lifecycle and data sources under supervision. | 0-2 | Banks: Analyst, Junior Analyst, Associate (asset servicing), Trainee, GraduateAsset servicers: Junior, Specialist (entry), Officer |
| L2 Independent | Owns analyses and reports for a business area, agrees definitions with business users, presents findings and trains new analysts on data and tools. | 2-5 | Banks: Analyst / Senior Analyst, AssociateAsset servicers: Specialist, Senior Specialist, Senior Officer |
| L3 Senior or specialist | Handles complex questions across several systems, designs metrics and data models, resolves data quality issues at the root and represents analytics in change and 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 banking analytics team, owns priorities, quality standards, access controls and stakeholder relations, and is the escalation point for data issues. | 8-14 | Banks: VP, Manager, Senior ManagerAsset servicers: Team Lead, Manager, Head of Section |
| L5 Leads the function | Accountable for analytics in a business line or region, sets the analytics strategy, tooling and standards in line with data governance, and reports 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 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
- Trade lifecycleWeight 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
- Securities settlementMain
- Trade capture and bookingMain
- Trade confirmation and affirmationMain
Where the role concentrates
How AI is changing the role
Generative AI writes much routine SQL and Python, and banks are adding natural language query tools to their data platforms, so basic extraction and reporting take less effort.
Read the full assessment
Generative AI writes much routine SQL and Python, and banks are adding natural language query tools to their data platforms, so basic extraction and reporting take less effort. The European Banking Authority reports growing AI use in banks together with expectations on data quality and governance. The World Economic Forum Future of Jobs Report 2025 names AI and big data as the fastest-growing skill. Value moves to understanding banking data correctly, framing questions, checking automated results and turning findings into process changes. Analysts who know the trade lifecycle and regulatory data rules keep an advantage.
Dated changes for this role
- ESMA guidelines on allocations and confirmations for T+1Relevant to you
- CSDR settlement discipline rules updated for T+1 in three stepsRelevant to you
- ECB Pontes: DLT settlement in central bank moneyRelevant to you
- Switzerland and Liechtenstein move to T+1 with the EU and UKRelevant to you
- EU securities settlement moves to T+1Relevant 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
- Data lineage
- ETL (extract, transform, load)
- Master data
- AnaCredit (Analytical Credit Datasets)
- Batch processing
- BCBS 239 (risk data aggregation)
- Critical data element (CDE)
- Data dictionary
All 24 concepts
- Data governance
- Data migration
- Data owner
- Data quality dimension
- Data quality rule
- Data steward
- Data warehouse
- DPM (Data Point Model)
- EBA taxonomy (reporting framework release)
- EMIR reporting
- FINREP (Financial Reporting)
- Golden source
- Metadata
- MiFIR transaction reporting
- SQL (Structured Query Language)
- Validation rules (EBA)
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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.
Trade lifecycleThe trade lifecycle is the sequence of steps a trade goes through from order to final maturity: execution, capture, enrichment, confirmation, clearing, settlement, reporting, lifecycle events such as resets, coupons and corporate actions, and termination. Front, middle and back office teams each own parts of it, with controls at each hand-off.
Reference dataReference data is the slowly changing data used to classify or describe other data: country and currency codes, industry classifications, calendars, instrument identifiers and legal entity identifiers. Errors in it spread across every system that uses it.
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.