Senior Data Analyst

Panera Bread•Saint Louis, LA

2d•$98,077 - $125,930•Hybrid

About The Position

The Senior Data Analyst is the person the business turns to when the numbers do not look right. This role combines deep, hands-on data investigation with formal accountability as the designated Data Steward for assigned business domains. When a stakeholder reports a data issue, this role owns the investigation end to end: tracing the metric back through the semantic layer, transformations, and pipelines to the source system, isolating exactly where the data broke, and driving it to resolution with the responsible team. Beyond individual issues, the Senior Data Analyst is accountable for the definitions, quality rules, and certification of the data that enterprise decisions depend on.

Requirements

Bachelor's degree in Analytics, Business, Computer Science, MIS, Economics, or a related field.
5+ years of experience in Data Analytics, Data Quality, or Data Governance.
Advanced SQL proficiency, including complex joins, window functions, and the ability to debug and reverse-engineer unfamiliar query logic against large datasets.
Demonstrated track record of independently investigating and resolving data discrepancies across multiple systems.
Working knowledge of data pipelines and ETL/ELT concepts sufficient to trace lineage and interpret transformation logic.
Demonstrated experience owning metric definitions, data quality, or data governance for a business domain.
Experience with data catalog and metadata tools, including documenting definitions, lineage, and ownership.
Experience defining and operationalizing data quality rules and monitoring.
Experience with modern cloud data platforms (BigQuery, Microsoft Fabric, or similar).
Strong understanding of dimensional modeling and semantic layer concepts.
Structured problem-solving skills and persistence in working an ambiguous issue to a definitive root cause.
Excellent written and verbal communication skills, including the ability to explain technical findings to non-technical stakeholders and influence without authority.
Python or R experience for data profiling and advanced analytics.
Experience mentoring or leading analysts.

Nice To Haves

Experience implementing data observability or automated anomaly detection tooling.
Experience implementing data classification, sensitive data discovery, or access governance in a cloud environment.
Familiarity with master data management concepts and reference data governance.
Experience defining data retention policies and supporting privacy or regulatory compliance requirements.
Restaurant, retail, or multi-unit operations data experience.

Responsibilities

Own end-to-end investigation of reported data issues, from initial stakeholder report through root cause identification and resolution.
Trace metrics backward through the semantic layer, transformation logic, and pipelines to the originating source system to isolate where a discrepancy is introduced.
Reconcile figures across systems and identify whether a variance stems from a source data problem, a transformation defect, a definitional difference, or expected business behavior.
Read and interpret existing SQL transformation logic to determine whether it reflects the intended business rule.
Profile source data to assess structure, completeness, cardinality, and fitness for use, and detect anomalies before they reach the business.
Distinguish genuine data defects from correct data that contradicts stakeholder expectations, and clearly explain the difference.
Document findings, root cause, business impact, and remediation for each material issue, and communicate status to affected stakeholders.
Identify recurring failure patterns and translate them into preventive data quality rules and monitoring.
Partner with business stakeholders to understand objectives and translate them into clearly defined metrics and analytical questions.
Perform deep-dive analyses to identify trends, drivers, anomalies, and root causes of business performance changes.
Build and maintain curated analytical datasets that downstream teams can consume with confidence.
Present findings, recommendations, and data quality issues to business leaders in a clear and compelling manner.
Serve as the accountable Data Steward for assigned data domains, owning business definitions, allowable values, and usage guidance.
Define, document, and certify KPIs and metric logic in the data catalog, and maintain definitions as business rules evolve.
Operate the KPI sign-off workflow, ensuring metrics are reviewed and approved by business and technical stewards before publication.
Establish and monitor data quality rules covering completeness, accuracy, timeliness, and freshness; triage and drive resolution of exceptions.
Maintain data lineage documentation and assess downstream impact of upstream changes before they are released.
Collaborate with Data Engineering to define upstream data requirements and drive fixes at the source rather than through downstream workarounds.
Partner with Security and Privacy teams to classify sensitive data, document retention requirements, and confirm appropriate access controls.
Serve as the escalation point and subject matter expert for data questions within the assigned domain.
Mentor analysts on investigative technique, governed metrics, and documentation standards.