Lead Business Operations Data Engineer - 928

Quantinuum•Broomfield, CO

1d•$146,000 - $183,000•Hybrid

About The Position

As a Lead Business Operations Data Engineer on the Analytics and Cost Estimating team within the Compute Platforms Group, you will own the design, delivery, and evolution of data analytics that inform resource planning, capacity management, and operational performance across the organization. This is a hybrid Analytics and Data Engineering role that covers the full data lifecycle from data ingestion and pipeline management to modeling, transformation, and the creation of executive-ready BI dashboards. The role acts as a bridge between raw data infrastructure and actionable business insights, ensuring operational data is translated into reliable, decision-ready intelligence and SOX compliant where applicable. You will partner closely with the CPG technical team, offering management, sales, finance, and business operations teams, working across enterprise systems and tools to ensure data accuracy, transparency, and measurable business impact. All applicants for placement in safety-sensitive positions will be required to submit to a pre-employment drug test.

Requirements

Bachelor’s degree minimum
Minimum 8+ years of experience in data engineering, analytics engineering, business intelligence, or operational analytics, including experience owning data pipelines, data models, and executive-facing reporting in a business-critical environment.
Due to Contractual requirements, must be a U.S. Person defined as, U.S. citizen permanent resident or green card holder, workers granted asylum or refugee status.
Due to national security requirements imposed by the U.S. Government, candidates for this position must not be a People's Republic of China national or Russian national unless the candidate is also a U.S. citizen.

Nice To Haves

Bachelor’s degree in analytics, business, engineering, computer science, or a related field, or equivalent practical experience
Advanced SQL proficiency, including complex joins, window functions, analytical modeling, query optimization, and performance tuning for structured analytical datasets
Hands-on experience designing, building, and maintaining production-grade ETL/ELT pipelines, transformations, and analytical models using modern data engineering practices
Experience with Python or another scripting language for data extraction, transformation, automation, testing, or operational analytics workflows
Experience working with cloud data warehouses, data platforms, or Lakehouse environments
Experience with analytics engineering and data modeling practices, including dimensional modeling, semantic layer design, reusable metrics, and self-service analytics enablement
Experience applying version control and software development practices, including Git-based workflows, code review, documentation, and repeatable deployment practices
Experience with data quality testing, validation, monitoring, lineage, and auditability to support trusted operational, executive, and compliance reporting
Experience building dashboards and reports for leadership audiences
Demonstrated ability to validate, audit, document, and explain data to ensure trust, accuracy, transparency, and decision readiness
Strong communication skills and comfort working cross-functionally across business and technical teams
Ability to define and influence technical standards for data modeling, pipeline development, documentation, testing, monitoring, and self-service analytics
Experience with delivering a wide range of types of analytics for different contexts
Familiarity with BI and visualization tools (e.g., Power BI, Tableau, Grafana, or equivalent)
Experience with dbt or similar analytics engineering frameworks for modular transformations, testing, documentation, and governed metric definitions
Experience with workflow orchestration or scheduling tools such as Airflow, Dagster, Prefect, Azure Data Factory, or equivalent
Experience supporting SOX, financial, compliance, or audit-sensitive reporting where data lineage, controls, and repeatability are required
Experience analyzing system reliability, usage, or operational performance metrics
Ability to proactively identify insights and recommend improvements—not just report data
Comfort working in environments where data spans operations, engineering, and business domains
Experience with optimizing data platforms for performance, cost, reliability, and scalability, and operational observability

Responsibilities

Partner with the Platform technical team and Offering Management on resource demand planning and capacity analytics, including platform systems usage, census forecasting, and materials and services needs to support customer and R&D deliverables with performance metrics versus plan
Analyze and report on machine uptime, throughput rates, job success/failure rates, and customer commitment fulfillment to support operational reliability and planning
Build and maintain time reporting and census analytics, auditing data for completeness and accuracy and providing actionable reporting for leadership
Own platform usage reporting, including: Platform utilization for SOX-compliant financial reporting and performance metrics, Active user and active project counts, System reliability indicators, Feature adoption and engagement metrics
Design, develop, and maintain scalable, reliable and efficient ETL / ELT pipelines that ingest data from operational systems and enterprise tools that support analytics, reporting, and operational decision-making.
Implement end-to-end monitoring, observability, and alerting data pipelines and platform health, proactively identifying and resolving data reliability issues before they impact users.
Architect and implement robust data models, and data integration solutions following industry best practices for performance, scalability, maintainability, and governance.
Model and transform raw data into analytics-ready tables and semantic layers, using analytics engineering best practices (e.g., dbt or similar frameworks)
Maintain and evolve data warehouses and reporting layers to support scalable, reliable analytics
Ensure data accuracy, lineage, documentation, and auditability, proactively resolving data quality issues
Partner with engineering and platform teams on driving data architecture standards, governance, access patterns, integrations, and engineering best practices
Deliver dashboards and datasets that enable self-service analytics while preserving consistency and trust
Communicate insights through clear narratives, visuals, and recommendations tailored to technical and non-technical audiences