Data Quality Analyst

We are looking for a Taxonomist

SUMMARY

We re looking for a hands-on Data Quality Analyst to understand our datasets, define and capture metadata, build & maintain taxonomies, and improve data quality. You will profile data, standardize definitions, clean and de-duplicate records, and make data easier to discover and trust across the organization.

RESPONSIBILITIES

- Data discovery and profiling: inventory sources, assess data health, completeness, and lineage; summarize patterns and anomalies

- Metadata modeling: define data dictionaries, business glossaries, and metadata schemas (technical and business); standardize naming conventions

- Taxonomy and classification: design and maintain controlled vocabularies, tagging schemes, and hierarchies; map synonyms; apply metadata at scale

- Data quality improvement: design validation rules, deduplication and standardization logic; implement profiling and monitoring; resolve data defects

- Canonical mapping: map disparate source fields to a common model; document transformations and provenance

- Cataloging and stewardship: populate and maintain the data catalog; implement lineage, ownership, usage notes, and data access classifications

- Collaboration: run stakeholder interviews to capture definitions and use cases; partner with data engineering/ML/BI to instrument metadata and checks in pipelines

- Documentation and enablement: publish standards, playbooks, and change logs; train teams on taxonomy usage and good metadata practices

- Enforce Compliance and ethics: apply retention, sensitivity, and privacy tags (e.g., PII); escalate risks and enforce access policies

REQUIRED EXPERIENCE

- 3+ years in data analysis, data stewardship, taxonomy, library/information science, or similar in a media & entertainment company

- Strong SQL experience with data profiling and transformation. Familiarity with Python or R for data cleaning is a plus

- Experience defining data dictionaries, glossaries, and metadata schemas; comfort with controlled vocabularies and classification

- Practical knowledge of data quality techniques: deduping, standardization, validation rules, and root-cause analysis

- Clear communicator who can translate between business and technical audiences; strong documentation habit

- Exposure to data catalogs (Alation, Collibra, Atlan, DataHub), taxonomy tools (PoolParty, SmartLogic, Synaptica), or graph/ontology basics. Familiarity with metadata standards (e.g., Dublin Core, schema.org) and data privacy (e.g., GDPR/CCPA).

SUCCESS METRICS

% of priority datasets cataloged with complete metadata and owners

Reduction in data quality issues (duplicates, nulls, invalid values)

Time-to-discover datasets/fields decreased; increase in catalog search success and usage

Adoption of taxonomy/controlled vocabularies across key teams

THE FIRST 90 DAYS

- Audit and profile the entire Apple TV+, Apple Music and App Store datasets; publish a lightweight data health report and create a backlog

- Stand up or improve a data dictionary/business glossary and agree on naming standards with stakeholders

- Define initial taxonomy and tagging guidelines; apply to the top datasets and iterate

- Implement a basic data quality rule sets

- Document processes and handoffs; recommend tool/process changes for scale

TOOLING EXPERIENCE

- SQL (Snowflake/BigQuery/Redshift/Postgres), spreadsheets

- Python or R for cleansing (pandas, dbt exposures if applicable)

- Data catalog/lineage tools (Alation, Collibra, Atlan, DataHub, OpenMetadata)

- Visualization for profiling/quality dashboards (Tableau, Power BI, Looker)

- Version control and tickets (Git, Jira)