layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Data Analyst at True Anomaly, Long Beach, CA

Independently partner with stakeholders to understand business objectives and translate them into reliable analyses, reports, dashboards, metrics, and data solutions.
Explore, clean, reconcile, and validate data from operational systems and emerging data infrastructure to identify trends, patterns, anomalies, and opportunities.
Build, maintain, and improve dashboards, recurring reports, SQL queries, analytical datasets, and ad hoc analyses that support business decision-making.
Test and execute new data models, reporting processes, and analytical workflows; identify issues, document findings, and provide feedback to improve scalability and usability.
Help establish consistent metrics, data definitions, documentation, quality checks, and reporting practices as our data capabilities scale.

4+ years of professional experience in data analysis, business intelligence, operations analysis, finance, product analytics, or a related field.
Demonstrated ability to independently own analytical projects from requirements gathering through analysis, validation, communication, and delivery.
Strong SQL skills, including complex joins, aggregations, CTEs, window functions, and the ability to investigate and validate query results.
Experience building and maintaining dashboards, reports, datasets, or analytical tools used by business stakeholders.
Strong problem-solving, communication, organization, and attention-to-detail skills, with the ability to exercise sound judgment when working with complex or imperfect data.
PREFERRED SKILLS AND EXPERIENCE
Experience with a BI or visualization tool such as Power BI, Tableau, Looker, Grafana, or a similar platform.
Experience validating data models, reporting pipelines, data transformations, or analytical workflows across multiple systems.
Familiarity with data modeling concepts such as tables, dimensions, measures, data grain, reusable reporting datasets, and metric definitions.
Exposure to Python, R, APIs, data warehouses, ETL/ELT, dbt, lakehouse concepts, Git, or other data development practices.
Experience improving data quality, automating manual reporting, documenting data processes, or helping establish analytics standards.

Data Analyst

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