AI Data Engineer (Hybrid)

Signet Jewelers•Irving, TX

23h•Hybrid

About The Position

We are seeking a highly skilled AI Data Analytics Engineer to design, develop, and support enterprise analytics and AI-enabled data solutions leveraging PostgreSQL, AWS Redshift, Microsoft Fabric, enterprise semantic models, and Power BI reporting platforms. This role will focus on building scalable analytics solutions that integrate enterprise operational and analytical data platforms while enabling advanced reporting, self-service analytics, and AI-driven business insights. The ideal candidate combines strong data engineering and analytics expertise with experience in modern cloud analytics architectures, semantic layer design, and AI-enabled reporting capabilities.

Requirements

Bachelors degree in Computer Science or related field
5+ years related experience

Responsibilities

Design and develop AI-enabled analytics solutions supporting enterprise reporting, forecasting, operational insights, and intelligent decision-making.
Support enterprise AI initiatives including: Generative AI integrations, AI-assisted analytics, Natural language reporting, Predictive analytics, Recommendation and insight generation capabilities.
Develop reusable analytics frameworks and scalable data products supporting enterprise reporting and AI use cases.
Identify opportunities to leverage AI capabilities to improve reporting development efficiency and business insight generation.
Design and implement AI-driven solutions that proactively identify: data anomalies, schema changes, integration failures, reporting inconsistencies, and data quality issues across enterprise platforms.
Develop intelligent monitoring and alerting frameworks that detect abnormal data patterns, missing data, volume fluctuations, and KPI deviations before impacting business operations.
Build AI-assisted development accelerators that reduce analytics and reporting development time through: automated code generation, reusable semantic modeling patterns, metadata-driven development, and AI-assisted testing and validation.
Implement machine learning and AI techniques to improve: data validation, matching accuracy, duplicate detection, and automated remediation workflows.
Create AI-enabled solutions that proactively analyze upstream system and schema changes to identify potential downstream reporting and integration impacts.
Develop automated data profiling and data lineage solutions that improve governance, transparency, and operational stability.
Leverage AI capabilities to streamline: report development, semantic model generation, SQL optimization, documentation, and operational support processes.
Support enterprise initiatives focused on intelligent automation and operational efficiency within data engineering and analytics platforms.
Collaborate with governance and architecture teams to establish standards for responsible AI usage within analytics and reporting ecosystems.
Evaluate emerging AI technologies and platforms to improve enterprise analytics capabilities, development efficiency, and business insight generation.