Analytics Engineer

• Build the core analytics data model – Design and maintain scalable fact and dimension models in our cloud data warehouse that support enterprise reporting and analysis.
• Create a trusted reporting layer across business systems – Integrate and model data from ERP, financial, project management, and operational platforms (e.g., NetSuite, Procore, Epicor, Snowflake, Timestream) to create a consistent source of truth.
• Preserve critical business context across systems – Identify where important financial or operational detail is lost in source systems or handoffs, and design data structures that retain the fidelity needed for analysis.
• Establish modeling standards and analytics best practices – Help define scalable approaches to naming, structure, metric logic, testing, and maintainability across the analytics environment.
• Deliver meaningful reporting and dashboards – Build and maintain dashboards and semantic models in our BI tooling that give stakeholders visibility into financial performance, project execution, and operational KPIs.
• Partner directly with stakeholders to solve ambiguous problems – Translate real business questions – such as cost overruns, efficiency gaps, and project performance – into clear datasets, metrics, and reporting solutions.
• Support planning and forecasting workflows – Maintain and improve planning and budgeting environments to help align planning, forecasting, and actual performance.
• Standardize KPIs across teams – Work with Finance, IT, Continuous Improvement, Operations, and other functions to define, document, and govern consistent metrics across the organization.

Senior Data Analytics Engineer

• Define, prototype, and productionize safety and performance metrics for automated driving systems.
• Establish analytic approaches and sufficiency criteria that support safety assessment, development decisions, and launch readiness.
• Support proactive safety monitoring and targeted investigations tied to specific system-performance or safety questions.
• Apply engineering and physics-based methods to process raw signals and derive meaningful representations of vehicle motion, driving context, and system behavior.
• Create interactive visualizations and reporting artifacts that communicate safety insights clearly, enhance transparency, and reduce barriers to interrogating source data in support of technical decision-making.

Analytics Engineer, AV Safety Engineering

• Design and develop data serving and semantic layers to support business needs ranging from traditional reporting to reverse ETL and LLM/AI implementations.
• Develop data models for use by analysts and applications from across the business using modern dimensional modeling practices to efficiently serve accurate, consistent data.
• Build and maintain robust, scalable data pipelines to extract, transform, and load (ETL, ELT) data from various sources into the data platform using both batch processing and event-driven patterns.
• Create efficient, performant data models to support business requirements and analytics needs.
• Implement and automate data quality checks and monitoring to ensure data accuracy and drive observability.
• Manage, troubleshoot and optimize existing data infrastructure, including cloud-based solutions and on-premises systems.
• Leverage cloud data platforms (Fabric, Databricks, Snowflake, etc.) for data storage, processing, and analysis.
• Work closely with data analysts, data scientists, and business stakeholders to understand their data needs and deliver solutions.
• Identify and resolve data-related issues and challenges for technical and non-technical stakeholders.
• Support company-wide projects ranging from server migrations to mergers and acquisitions as a data and systems expert.
• Automate data processes to improve efficiency and reduce manual effort.
• Implement modern concepts like metadata-driven pipelines and Infrastructure-as-Code that will play a key role in a scalable, resilient data platform.
• Document data processes, architecture, and workflows.

• Analyze sales, repair, and upgrade performance data to identify trends, opportunities, and risks across the US & Canada field organizations
• Design and develop dashboards, reports, and data visualizations that translate complex data into clear, actionable insights for Sales, Field Operations, and Leadership
• Develop working fluency in the elevator business: unit profiles, component knowledge, contract basics, and the KPIs that drive rep behavior
• Build and maintain data models that support long ‑ term strategy for repair revenue growth, customer conversion, and sales productivity
• Conduct deep ‑ dive analytics on customer behavior, portfolio performance, and service history to recommend targeted upgrade and repair strategies
• Support business case development for new repair programs, pricing initiatives, and process improvements using data ‑ driven insights
• Ensure data accuracy, governance, and integrity across multiple sources
• Contribute to metadata documentation efforts
• Evaluate business processes to identify areas for AI workflow optimization

Business Analytics Engineer

• Build, maintain, & optimize ETL data pipelines
• Build, maintain, & optimize corporate semantic models
• Address IT tickets & troubleshoot system performance
• Perform administrative duties to maintain the current data environment
• Assist, as needed, with the development of reports and other solutions
• Work with stakeholders throughout solution lifecycle
• Improve platform stability, data value & quality, and promote a data culture
• Actively collaborate with teammates to share workloads, develop skills, & strengthen the team

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

Data Analytics Engineer

• Architect, Design and implement ETL processes.
• Develop and maintain integration tools and utilities.
• Proficient in writing SQL queries and troubleshooting issues.
• Support existing applications.
• Provide guidance and support to Junior/Mid-level developers.
• Instruct, direct, and check the work of others.
• Lead, design, code, test and debug complex development efforts.
• Document, enforce and model best standards and practices to others.
• Oversee project completions and ensure user satisfaction.
• Complies with Company policies, procedures, regulatory requirements, and complete mandatory company compliance training by prescribed deadlines.
• Ability to accept guidance and supervision from management.
• Other duties that may be reasonably assigned.

Senior ETL Developer

• Own the design and delivery of SafetyChain's analytics and reporting capabilities
• Architect and manage analytics infrastructure across Microsoft Fabric and Snowflake
• Build reliable, well-modeled data pipelines
• Design and deliver dashboards and reports for operational visibility
• Define data models, metrics, and KPIs
• Identify opportunities to apply AI and ML to analytics use cases
• Establish standards for data quality, governance, and documentation

Senior Analytics Engineer, Snowflake, MS Fabric

• Lead, coach, and provide technical guidance to a team of analytics engineers
• Design and oversee scalable data pipelines, models, and architecture
• Partner with business and analytics stakeholders to turn complex questions into usable data solutions
• Build and automate data extraction, transformation, and loading (ETL) processes using industry standards
• Improve existing data pipelines by identifying performance, reliability, and quality issues
• Develop tools and standards to improve code quality, consistency, and development speed
• Create clear documentation to support long-term sustainability
• Collaborate closely with data visualization and reporting teams

Manager, Analytics Engineering

• Build and maintain dbt models on the Pacific Postgres warehouse, fed by Stitch and Fivetran
• Develop PowerBI reports and SQL analyses for internal stakeholders: sales operations, financial forecasting, KPIs, executive scorecards
• Build and extend ThoughtSpot Liveboards embedded inside the Perform customer portal, delivering Pacific-powered analytics directly to customers
• Partner with Product, Sales Operations, and Finance to define KPIs across Procure and Perform — internal and customer-facing
• Own data quality, testing, and documentation hygiene across dbt and downstream BI
• Contribute to ELT pipeline reliability — source health, refresh cadence, schema change management
• Use Claude as a core part of your workflow — AI-assisted SQL and dbt development, data Q&A, documentation, accelerated report iteration

• Develop and maintain ETL processes to support data ingestion, transformation, and integration.
• Work with various file formats (TXT, CSV, XML, JSON, YAML) to ensure seamless data processing.
• Implement and optimize ETL workflows using EasyMorph, Databricks, Azure Data Factory, Talend, and Apache Nifi.
• Write clean, efficient, and maintainable code using Python, C#, or Java.
• Ensure data quality, accuracy, and integrity across ETL pipelines.
• Collaborate with data engineers and analysts to define and refine ETL requirements.
• Troubleshoot and resolve ETL failures, ensuring minimal downtime and impact.
• Follow test-driven development (TDD) methodologies to improve reliability and maintainability.
• Maintain and document ETL processes, providing insights and recommendations for improvements.

Associate ETL Engineer

• Build and maintain dbt models that transform raw data from multiple sources into clean, tested, well-documented datasets.
• Partner with data scientists, engineers, and product teams to translate ambiguous questions into durable data models rather than one-off queries.
• Improve data quality across the stack by writing tests, defining expectations for critical models, and triaging issues when something looks wrong.
• Document models, metrics, and lineage so engineers, analysts, and partner organizations can self-serve with confidence.
• Use Python where it's the right tool — for orchestration, ad-hoc work, or transformations that don't belong in dbt.
• Raise the bar on analytics engineering practices, including code review, modeling conventions, CI for data, and documentation standards.
• Perform additional engineering and data duties as needed to support the broader team.

• Design and develop data Integrations to deliver Enterprise BI Data Marts and BI Semantic Data layer(s).
• Manage multiple projects within the team and support others within the DAS organization.
• Improve data processes, technology infrastructure and reporting/analytical services.

Senior Analytics Engineer

• Design and develop data integrations to deliver Enterprise BI Data Marts and BI Semantic Data Layer(s)
• Develop modern data engineering pipelines leveraging AI assisted capabilities
• Lead multiple projects and provide technical guidance to team members
• Support other team members within the DAS organization
• Improve data processes, technology infrastructure, and reporting and analytical services

Lead Analytics Engineer

• Lead, coach, and provide technical guidance to a team of analytics engineers
• Design and oversee scalable data pipelines, models, and architecture
• Partner with business and analytics stakeholders to turn complex questions into usable data solutions
• Build and automate data extraction, transformation, and loading (ETL) processes using industry standards
• Improve existing data pipelines by identifying performance, reliability, and quality issues
• Develop tools and standards to improve code quality, consistency, and development speed
• Create clear documentation to support long‑term sustainability
• Collaborate closely with data visualization and reporting teams

• Lead the end-to-end delivery of complex data engineering projects, including requirements definition with business stakeholders, technical design, implementation oversight, and delivery coordination with data engineering, analytics, and AI/ML teams
• Serve as the primary technical liaison between business partners and engineering teams, translating business requirements into well-scoped, executable data initiatives with clear outcomes
• Architect and develop cloud-native data solutions on AWS, including the design and deployment of data pipelines using AWS Glue, Airflow (Amazon MWAA), Step Functions, Lambda, and related services
• Lead the migration and modernization of existing data workflows, including the conversion of RPA-based automation into cloud-native orchestration solutions using AWS Airflow
• Design, develop, and deploy RESTful and authenticated APIs on Kubernetes (K8s) to expose data services and other reusable data products consumed by downstream systems
• Oversee and contribute to infrastructure-as-code practices using Terraform, ensuring analytics and data engineering workloads are provisioned, managed, and repeatable across environments
• Establish and enforce data pipeline best practices including data quality checks, lineage tracking, and governance standards across all projects under this role's scope
• Monitor, observe, and maintain production data systems and pipelines, using tools such as CloudWatch, DataDog, or Grafana as applicable
• Conduct exploratory and operational data analysis to surface deficiencies, validate pipeline outputs, and support data-driven decision making for business stakeholders
• Define and communicate data architecture standards and patterns to both technical and non-technical audiences, including documentation of architectural decisions and pipeline design
• Collaborate on cross-cutting concerns such as data security, infrastructure alignment, and platform strategy
• Coordinate with DevOps and infrastructure teams to ensure that data engineering deliverables align with CI/CD pipelines, containerization standards, and broader organizational engineering practices

Principal Data Analytics Engineer

• Design and build complex, scalable data products that support internal analytics and power product features, using tools like dbt, Snowflake, and Dagster.
• Translate ambiguous business needs into structured, trustworthy data assets—owning development end-to-end, from design through deployment and monitoring.
• Contribute to shared platform tooling that improves pipeline orchestration, testing, access control, and observability for both engineers and analysts.
• Evaluate and implement new platform capabilities, such as Semantic Views, Cortex, or SnowML, to improve performance, maintainability, and user experience.
• Support and enable teammates across the org, including onboarding new users, resolving data issues, and documenting patterns and best practices.
• Maintain reliability and data quality through shield support rotations, strong monitoring, alerting, and test coverage.

Senior Consumer Analytics Engineer

• Maintain and expand the company’s analytical data model using Snowflake and dbt, ensuring datasets are reliable, well-structured, and easy to use.
• Partner with stakeholders to understand reporting and analytics needs and translate them into new models and datasets.
• Investigate discrepancies in metrics and datasets and perform root-cause analysis across systems.
• Monitor and maintain ELT pipelines across our data stack.
• Investigate and resolve pipeline failures, schema changes, and data inconsistencies.
• Identify opportunities to improve pipeline reliability, efficiency, and cost effectiveness.
• Expand documentation across the data model to clearly describe business logic, relationships, and definitions.
• Ensure datasets are clearly structured and documented so they can be reliably used across analytics tools and internal workflows.
• Contribute to the structured AI data context files that help internal AI tools accurately interpret datasets and metrics.
• Help maintain data governance standards, including contributing to PII masking policies and ensuring sensitive customer data is handled appropriately across the data platform.
• Work closely with Product, Revenue, and Operations teams to understand their data needs and questions.
• Help stakeholders navigate the data model and identify the most appropriate datasets for their use cases.
• Occasionally build or modify Hex projects to support data exploration or reporting needs.

• Solve analytical problems quickly and accurately
• Use Excel, Python, and SQL to meet real‑time business needs
• Own execution from vague question to clear, actionable output
• Transform one-off analyses into reusable reporting and analytics assets
• Help shape the evolution of a broader analytics and information platform
• Treat analytics as a product that must scale, adapt, and improve continuously
• Act as a trusted, client-facing analytics partner across the business
• Build durable relationships through clarity, approachability, and follow-through
• Proactively identify opportunities where analytics can drive better decisions
• Collaborate effectively with data scientists and technical teams
• Communicate fluently about analytical methods and implications
• Apply architectural thinking without needing to formally own platform engineering

• Manage the learning analytics platform and dashboard lifecycle, including backlog development, dependency coordination, and quality assurance
• Lead visualization and User Experience/User Interface (UX/UI) design discussions, providing guidance and recommendations to improve clarity, usability, and performance
• Gather, analyze, and document requirements, and translate them into user stories, use cases, and analytics specifications
• Plan and facilitate stakeholder interviews, discovery workshops, and design sessions to understand business processes and analytics needs
• Design, develop, and maintain complex analytic Power BI dashboards and reports to measure engagement, utilization, performance, ROI, and impact
• Translate learner usage data into insights that improve learning journeys, content effectiveness, and user experience
• Develop and optimize SQL queries, data models, and integrations across multiple platforms and data assets
• Identify data anomalies, recommend corrective actions, and assess the downstream impact of changes across platforms or teams
• Develop and execute testing and quality assurance plans to validate data accuracy and analytic outcomes
• Evaluate and apply ChatGPT Artificial Intelligence (AI) technologies to enhance learning dashboards, analytics automation, and reporting capabilities.

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