AI Data Engineer - Data Analytics & Business Intelligence

Blueprint Technologies•Redmond, WA

19h•$135,000 - $145,000•Remote

About The Position

As a Data Analytics & Business Intelligence professional, you will play a key role in transforming complex operational and technical data into trusted, actionable insights that drive performance, improve decision-making, and enhance organizational efficiency. Working cross-functionally with engineering, operations, program management, and business stakeholders, you will be responsible for ensuring data quality and integrity, developing scalable reporting solutions, automating manual processes, and creating dashboards that provide visibility into critical business and operational outcomes. This role is ideal for someone who enjoys solving data challenges, identifying trends and opportunities, and leveraging analytics to support continuous improvement in a fast-paced, mission-critical environment.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Data Analytics, or a related technical field.
5–7 years of professional experience in data analytics, business intelligence, reporting, data quality management, or a related discipline.
4+ years of experience conducting data quality validation and verification to ensure data accuracy, completeness, and consistency across multiple systems and sources.
4+ years of experience analyzing large datasets to identify trends, anomalies, risks, gaps, and opportunities for improvement.
3+ years of experience developing dashboards, reports, and visualizations using Power BI, Tableau, or similar business intelligence platforms.
Experience designing and implementing data quality monitoring, validation, and governance processes.
Experience automating manual workflows using scripting, workflow automation, or data integration technologies.
Strong analytical, troubleshooting, problem-solving, and investigative skills.
Excellent written and verbal communication skills with the ability to present complex findings to both technical and non-technical audiences.
Demonstrated success delivering scalable solutions that improve operational visibility, efficiency, and data reliability.

Nice To Haves

Experience working with telemetry, operational, engineering, IoT, industrial controls, critical infrastructure, or comparable technical data environments.
Experience implementing automated data validation, exception management, and monitoring processes.
Experience developing executive-level dashboards, KPI scorecards, and performance reporting frameworks.
Experience supporting mission-critical operational environments where data quality and reliability are essential.
Familiarity with data governance, metadata management, and enterprise reporting best practices.
Experience partnering with cross-functional technical and business teams to drive data-informed decision-making.

Responsibilities

Partner with engineering, operations, program management, and business stakeholders to understand objectives, define reporting requirements, and deliver analytics solutions that support informed decision-making.
Analyze large, complex datasets to identify trends, anomalies, performance gaps, risks, and opportunities for operational improvement.
Conduct data quality assessments, validation, and verification activities to ensure accuracy, completeness, consistency, and integrity across multiple data sources.
Design, implement, automate, and maintain scalable data collection, transformation, validation, and reporting processes.
Develop and maintain dashboards, KPI scorecards, reports, and self-service analytics solutions that provide visibility into operational performance, program health, technical metrics, and business outcomes.
Build data validation frameworks, monitoring processes, and quality controls to proactively identify and resolve data issues.
Collaborate with technical teams to enhance data models, reporting structures, metadata management practices, and overall data governance initiatives.
Translate complex technical findings into clear, concise, and actionable recommendations for both technical and executive audiences.
Drive process improvements through automation, standardization, and implementation of scalable analytics and reporting solutions.
Support root cause investigations by leveraging data to identify drivers of operational issues, risks, and performance challenges.
Document methodologies, business requirements, reporting processes, and data quality standards to ensure consistency and scalability.