Senior Data Analyst job description

Data and analytics — Senior Data Analyst
Leads complex analyses and sets the standard other analysts follow.

RESPONSIBILITIES
- Redesign a model that has drifted from the business it describes, migrating its consumers without breaking their reports.
- Set the metric governance process for a domain, including how a new metric gets approved and where its single source of truth lives.
- Design the statistical approach for a complex or contested analysis, and catch a misuse of statistics in someone else's work before it ships.
- Set the experimentation standards for a product area, including guardrail metrics, and stop a launch being pushed through on a cherry-picked result.
- Build, validate and compare models on structured or text data, using metrics that match the business decision.
- Diagnose and rewrite a query that is timing out a production dashboard.

REQUIREMENTS
- Experience at the Data Analyst level (level 2 of 5) or equivalent
- Holds AWS Certified Data Engineer - Associate and Salesforce Certified Tableau Desktop Foundations

REQUIRED SKILLS
Data Engineering
- Analytical SQL Querying — You can diagnose and rewrite a query that is timing out a production dashboard, and you set the SQL style and review standards your team follows.
- Dimensional Data Modeling — You can redesign a model that has drifted from the business it describes, migrating its consumers without breaking their reports.
- Data Pipeline Development — You can design the pipeline architecture for a new data domain, including how failures are surfaced and how a bad run is safely reversed.
- ETL/ELT Transformation Design — You can design a transformation layer for a new dataset, choosing what to compute once centrally versus leave to the query, and test it against known-good output.
- Data Warehouse Architecture — You can design the layering, partitioning and access pattern for a new subject area, balancing query speed against storage and compute cost.

Data Governance
- Data Quality and Pipeline Monitoring — You can trace a failure through a chain of dependent pipelines to its source, and set which checks block a run and which only warn.
- Metric Definition and Governance — You can set the metric governance process for a domain, including how a new metric gets approved and where its single source of truth lives.
- Data Lineage and Documentation — You can map lineage across a full transformation chain and write documentation another analyst uses without coming back to you. You record who a dataset is for and what it should not be used for.
- Data Ethics and Privacy — You can design roles and retention for a dataset so it holds only what its purpose needs, and answer a subject access or deletion request against it.

Statistics & ML
- Statistical Analysis and Inference — You can design the statistical approach for a complex or contested analysis, and catch a misuse of statistics in someone else's work before it ships.
- Experiment Design and A/B Testing — You can set the experimentation standards for a product area, including guardrail metrics, and stop a launch being pushed through on a cherry-picked result.
- Predictive Modeling and Machine Learning — You can build, validate and compare models on structured or text data, using metrics that match the business decision.
- Feature Engineering — You can decide which features a model needs from the data available, test whether they actually improve it, and drop the ones that do not earn their place. You keep them interpretable enough to explain to the business.
- Forecasting and Trend Analysis — You can pick and justify an approach for a series you have not forecast before, and state the uncertainty in terms a decision maker can use. You say when the history is too thin to forecast at all.
- Data Exploration and Preparation — You can link two or more sources, deduplicate the records and weight the sample, then state what the prepared data can and cannot support.
- Programming for Analysis — You can review shared analysis code and improve its tests, structure and documentation so other analysts can reuse it.

Visualization
- Dashboard Design — You can set the dashboard standards for a domain, covering performance, shared definitions and accessibility, and retire dashboards nobody uses.
- Data Visualization Best Practice — You can choose encodings that make the real pattern visible without exaggerating it, and design a chart a reader reads correctly at first glance. You defend a plain chart over a striking one when the plain one is more honest.
- Stakeholder Analytics Translation — You can agree a project plan with senior stakeholders, covering scope, milestones and what the data can and cannot tell them.
- Analytics Storytelling and Reporting — You can structure a report so a busy reader gets the point in the first minute and can act on a stated recommendation.

Delivery
- Project Management — You can coordinate a small team's work on a shared goal: who does what by when, what has to wait for what, and a revised plan when things change.
- Planning & Estimation — You can estimate a whole project including its risks and unknowns, split it into stages, and defend the estimate when it is challenged.
- Ownership & Accountability — You can hold accountability for outcomes delivered mostly by other people, taking the blame when it fails and passing on the credit when it works.
- Quality Focus — You can set how quality is checked on complex work shared by several teams, and you spot the problems that only appear where their pieces meet.

Craft
- Problem Solving — You can make the call on a problem outside your expertise and explain its cost, time and risk.
- Domain Expertise — You can advise the team on your area and apply new developments in the field where they pay off.
- Continuous Learning — You can judge which new ideas are worth the team's time and which are not, and you make room for the people around you to learn too.

Communication
- Communication — You can bring disagreeing groups to agreement on the facts and the next steps, and help colleagues frame a difficult or sensitive message.
- Collaboration — You can align teams with competing priorities on a common goal, surfacing the conflict early instead of letting it harden into resentment.
- Writing & Documentation — You can own the documents behind a large piece of work, coordinating contributions so that people who were not involved can carry it on.
- Stakeholder Management — You can win support for a proposal, reset expectations when the plan changes, and turn down a request with a reason the other side accepts.

Leadership
- Leadership — You can lead one piece of work shared by several people, on your own team and beyond, and make calls that they act on.
- Mentoring — You can mentor someone over months, tell them the uncomfortable thing they need to hear, and adapt how you teach to how they learn.
- Strategic Thinking — You can look a year ahead in your area, name what will matter by then, and turn that into work people can pick up now.

REQUIRED CERTIFICATIONS
- Salesforce Certified Tableau Desktop Foundations (required from Data Analyst)
- AWS Certified Data Engineer - Associate (required from Senior Data Analyst)