Junior Data Analyst job description

Data and analytics — Junior Data Analyst
Runs defined analyses and dashboards under close guidance.

REQUIRED SKILLS
Data Engineering
- Analytical SQL Querying — You can join multiple tables, use window functions for running totals and rankings, and spot when a join is silently duplicating rows.
- Dimensional Data Modelling — You can read an existing star schema and say which table holds a given fact or attribute.
- Data Pipeline Development — You can run and troubleshoot an existing pipeline job from its logs, following a runbook.
- ETL/ELT Transformation Design — You can apply a documented cleaning rule, such as trimming a field or standardising a date format, inside an existing transformation.
- Data Warehouse Architecture — You can explain the layers of the warehouse you work in and say which layer a given table lives in.
- Data Pipeline Monitoring and Alerting — You can check whether a scheduled job ran and read its logs to see where it failed. You add a row-count or freshness check to a pipeline when someone shows you the pattern to follow.

Data Governance
- Data Quality Monitoring — You can write a straightforward completeness or range check for a dataset you know well, and investigate the first likely cause of a failure.
- Metric Definition and Governance — You can look up an existing metric definition and calculate it correctly for a simple request.
- Data Lineage and Documentation — You can trace one field back to the table it came from with help, and write up what a dataset holds using the team template. You ask the owner when the meaning of a column is not obvious.
- Data Privacy and Compliance — You can classify a new dataset's sensitivity correctly and apply the masking or access restriction its classification requires.
- Role-Based Data Access Control — You can grant or revoke access using an existing role once a request is approved, and you check that approval first. You raise anything touching sensitive data with someone senior before acting.

Statistics & ML
- Statistical Analysis and Inference — You can calculate summary statistics such as mean, median and standard deviation, and describe what a simple distribution looks like.
- Experiment Design and A/B Testing — You can read an experiment results dashboard and say whether the change is winning, losing or inconclusive against its stated metric.
- Predictive Modelling Fundamentals — You can describe, in plain terms, the difference between a regression problem and a classification problem.
- Feature Engineering — You can build the features a colleague has specified, such as a ratio, a lag or a bucketed field, and sanity-check the result. You ask before inventing a transformation of your own.
- Forecasting and Trend Analysis — You can produce a forecast using a method someone has chosen for you and plot it against what actually happened. You notice when the output looks obviously wrong and say so rather than passing it on.

Visualisation
- Dashboard Design — You can build a small dashboard from a clear brief, choosing chart types that suit the data and labelling it so a first-time viewer understands it.
- Data Visualisation Best Practice — You can build a chart to a given specification using the agreed colours and labels. You label axes and units without being reminded, and you ask before choosing an encoding yourself.
- Exploratory Data Analysis — You can profile a dataset with the checks you have been given, such as row counts, distributions and missing values, and report what you found. You flag the oddities rather than deciding alone what they mean.
- Stakeholder Analytics Translation — You can ask enough clarifying questions to turn a vague request into a concrete analysis, and present the result without unexplained jargon.
- Analytics Storytelling and Reporting — You can write up an analysis in the team format, leading with the finding rather than the method. You get it reviewed before it reaches anyone outside the team.

Delivery
- Project Management — You can follow a plan someone else wrote, keep your own tasks up to date, and flag a task you own as soon as you know it will slip.
- Planning & Estimation — You can estimate a task you have been given once the approach is clear, and you say promptly when the estimate turns out to be wrong.
- Ownership & Accountability — You can own a piece of work after release: you watch how it behaves, fix what you broke, and are never chased for a status update.
- Quality Focus — You can choose checks that suit the work in front of you, catch your own mistakes before review, and confirm the result behaves once it is live.

Craft
- Problem Solving — You can diagnose problems in work you did not build, and you fix the underlying cause instead of routing around the symptom.
- Domain Expertise — You can apply the basics of your field to the work you are given, and you know who to consult at the edge of what you know.
- Continuous Learning — You can pick up an unfamiliar area fast enough to be useful in it, and you turn what you learned into something others can reuse.

Communication
- Communication — You can explain your work to your team so they can act on it, and you take vague requirements and ask the questions that sharpen them.
- Collaboration — You can work well with people in other roles, share context and credit, and take criticism of your work without defending territory.
- Technical Writing — You can document your own work well enough for a colleague to follow it unaided, and you keep that document current as the work changes.
- Stakeholder Management — You can keep your manager and immediate team informed of your progress, and you raise an issue before someone else discovers it.

Leadership
- Leadership — You can act on direction well, seek feedback on how you work, and be transparent about what you need help with.
- Mentoring — You can share what you have just learned with your peers and help a newer colleague find their way around.
- Strategic Thinking — You can explain why the work you were given matters and who it is for.