Role Description We are looking for a Corporate Data Architect who will help organize and standardize how the organization understands and uses data. Your main task will be to design and develop data architecture at the level of the entire organization so that data is consistent, well defined, and ready to be used in analytics, AI, and system integrations. You will act as a bridge between the worlds of business, data, and technology, connecting strategy, standards, and practice. - Design semantic, logical, and conceptual data models based on business domains and organizational processes. - Define and standardize the corporate data model as part of the Data Governance initiative. - Create and maintain reference architecture (logical and technical reference architectures) and design patterns for data domains. - Co-create the strategy and roadmap for data architecture development that supports the vision of the Data Governance program and organizational goals. - Collaborate with Data Governance, Data Management, Analytics, AI, and Data Engineering teams to integrate models with the data catalog and metadata management tools. - Support technical teams in implementing models in data layers. - Participate in designing data integration, flow, and quality processes (data lineage, data contracts, data quality). - Identify and certify authoritative data sources within organizational domains. - Monitor the compliance of data solutions with Data Governance policies and standards as well as architectural recommendations. - Support the resolution of data conflicts and issues. - Create architectural documentation, metamodels, and inter-domain relationship diagrams. - Consult on and develop good practices in the area of modeling, metadata, and information architecture. - Collaborate on defining the vision, priorities, and directions for the development of data architecture in the organization. Qualifications - Experience in data modeling (conceptual, logical, physical), preferably in a large organization or within complex data ecosystems. - Practical knowledge of tools and methodologies such as ER, UML, IDEF1X, Data Vault, 3NF, Kimball/Inmon. - Ability to work with data catalogs and metadata management tools (e.g. DataHub, Collibra, Alation, Atlan). - Knowledge of SQL and relational data models. - Experience working with database systems and data architecture in a cloud environment (AWS). - Ability to work with engineering and analytics teams to translate business needs into data models. - Understanding of data security, availability, and control principles. - Good command of English (reading documentation, community discussions). Requirements - Experience in Data Governance, Data Quality, Master Data, and Metadata Management projects. - Knowledge of concepts and technologies related to the semantic data layer: ontologies, RDF, OWL, GraphQL, dbt Semantic Layer, Semantic Kernel. - Knowledge of Python for automation, model validation, and API integrations. - Knowledge of event-driven data architecture. - Experience in creating and maintaining architectural documentation. Benefits - Flexible employment and remote work. - International projects with leading global clients. - International business trips. - Non-corporate atmosphere. - Language classes. - Internal & external training. - Private healthcare and insurance. - Multisport card. - Well-being initiatives.

Title: Staff Data Engineer Location: Denver, Colorado, United States; San Francisco, California, United States Job Description: About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, Amazon, and Anthropic. We're a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company. As a Staff Data Engineer on the People Data team, you''ll help build and evolve the centralized platform that powers every Checkr product. This platform stores and serves the identity and people records that underpin Checkr''s Workforce, Mortgage, Tenant, Trust, and Personal products, making it foundational to the company''s AI-powered verification platform and every high-stakes decision our customers make. In this role, you''ll own the core services, data pipelines, and architecture that keep this platform scalable, reliable, and ready for the next generation of Checkr products. You''ll solve complex distributed systems and data engineering challenges while influencing the technical direction of one of the company''s most critical platforms. What you''ll do - Architect, design, lead, and build an end-to-end, performant, reliable, scalable data platform. - Work as an independent contributor: solve problems and deliver high-quality solutions with minimal oversight and strong ownership. - Mentor and guide junior engineers to deliver complex, next-generation features. - Bring a customer-centric, product-oriented mindset. Collaborate with customers and internal stakeholders to resolve product ambiguities and ship features that solve real customer problems. - Partner with engineering, product, design, and other stakeholders to design and architect new features. - Experimentation mindset: autonomy and empowerment to validate a customer need, get team buy-in, and ship a rapid MVP. - Quality mindset: you treat quality as a non-negotiable part of your software deliverables. - Analytical mindset: instrument and deploy new product experiments with a data-driven approach. - Monitor, triage, and resolve production issues for the team''s services. - Create and maintain data pipelines and foundational datasets to support product and business needs. What you bring Required Experience - 10+ years designing, implementing, and delivering highly scalable, performant data platforms. - Experience building large-scale data processing pipelines using ETL/ELT, batch, and stream processing. - Expert-level proficiency in PySpark, Python, and SQL. - Expertise in data modeling, relational databases, and NoSQL data stores (e.g., MongoDB). - Experience with big data technologies such as Kafka, Spark, Iceberg, data lakes, and the AWS stack (EKS, EMR, Serverless, Glue, Athena, S3, etc.). - Knowledge of security best practices and data privacy concerns. - Strong problem-solving skills and attention to detail. Nice to have - Experience or knowledge of data processing platforms such as Databricks or Snowflake. #LI-TD1 Pay Transparency Disclosure We use geographic cost of labor as an input to develop ranges for our roles and as such, each location where we hire may have a different range. If this role is remote, we have listed the top to the bottom of the possible range, but we will specify the target range for an exact location when you are selected for a recruiting discussion. For more information on our compensation philosophy, see our website. On-target Earnings OR Base Salary range (San Francisco, CA) $196,000-$230,000 USD On-target Earnings OR Base Salary range (Denver, CO) $166,000-$195,000 USD What We Offer - A fast-paced and collaborative environment - Learning and development allowance - Competitive cash and equity compensation, and opportunity for advancement - 100% medical, dental, and vision coverage - Up to $25K reimbursement for fertility, adoption, and parental planning services - Flexible PTO policy - Monthly wellness stipend At Checkr, we believe an in office work environment strengthens collaboration, drives innovation, and encourages connection. Our hub locations are Denver, CO; San Francisco, CA; Nashville, TN; and Santiago, Chile. Individuals are expected to work from the office 3+ days a week. In-office perks are provided, such as lunch five times a week, a commuter stipend, and an abundance of snacks and beverages. A Equal Employment Opportunities at Checkr Checkr is committed to building the best product and company, which requires hiring talented and qualified individuals with a diverse set of perspectives and lived experiences. Checkr believes in hiring people of all backgrounds, including those whose histories are impacted by the justice system in accordance with local, state, and/or federal laws, including the San Francisco's Fair Chance Ordinance.

Data Engineer Department: IT Job Description: Employment Type: Full Time Location: Remote- USA Compensation: $116,000 - $155,000 / year Description Trilon is building a supercharged, technology-enabled future for our people and partners. The Data Engineer plays a key role in that mission by building and maintaining the data platform that powers Trilon's enterprise analytics, automation, and AI capabilities. Reporting to the Vice President, Data & DevOps, this role is responsible for designing, developing, and maintaining scalable data integrations and transformations in Azure and Microsoft Fabric. The Data Engineer ensures that Trilon's data platform delivers reliable, high-quality, and well-structured data to support business intelligence, operations, and innovation. This role serves as the primary custodian of Trilon's integrated data model and is instrumental in developing a unified, extensible architecture that scales with continued acquisitions. The Data Engineer designs and builds secure Power BI semantic models for consumption by analysts and decision-makers, ensuring consistent and governed access to enterprise data. This role also partners closely with the AI and Innovation vTeam to prepare data for analytics, machine learning, and retrieval-augmented generation (RAG) applications. Key Responsibilities Data Platform Engineering and Maintenance - Serve as the primary owner and technical steward of the Trilon enterprise data platform - Design, develop, and maintain data pipelines and workflows using Azure Data Factory, Synapse, and Microsoft Fabric - Build and manage data transformations, orchestration, and automation across structured, semi-structured, and unstructured data sources - Ensure scalability, reliability, and performance of the data platform as Trilon continues to grow through acquisition - Implement monitoring and alerting to proactively detect and resolve pipeline or data quality issues Data Integration and Modeling - Develop and maintain integrations between Trilon's enterprise systems, cloud services, and acquired partner environments - Design and maintain a unified, scalable data model that harmonizes data across business systems - Build secure, governed, and high-performance Power BI semantic models optimized for analytics and self-service reporting - Collaborate with business analysts and data consumers to ensure data models support enterprise reporting needs and KPIs - Partner with cybersecurity and infrastructure teams to ensure data models and access patterns meet compliance and governance standards Data Quality and Governance - Implement validation and quality checks to ensure accuracy, completeness, and timeliness of enterprise data sets - Maintain metadata, lineage, and documentation to promote transparency and reusability - Define and enforce data quality and consistency standards across all integrated sources - Collaborate with the Technology Asset Manager and Service Platform Manager to align system integrations and data governance - Support data cataloging, discovery, and classification initiatives within Microsoft Purview or equivalent tools Automation, Optimization, and Resilience - Develop automated frameworks for ingestion, transformation, and validation using Azure-native tools and pipelines - Implement DevOps principles for data workflows including version control, testing, and deployment automation - Optimize pipeline performance, resource utilization, and data freshness - Build resilience and fault tolerance into data operations to ensure reliability and recovery - Create reusable components and templates to streamline integration of new data sources and partner systems AI and Innovation Enablement - Collaborate with the AI and Innovation vTeam to prepare and structure data for AI, ML, and RAG-based applications - Develop and maintain data pipelines that support model training, evaluation, and fine-tuning - Curate and transform unstructured data for retrieval, embedding, and vectorization within AI applications - Ensure data readiness for generative AI tools, chat interfaces, and knowledge retrieval systems - Stay informed of emerging AI data engineering trends and Microsoft Fabric AI integrations Collaboration and Cross-Domain Partnership - Partner with application and infrastructure teams to ensure reliable and secure data exchange across systems - Collaborate with business stakeholders and analysts to understand reporting needs and deliver usable data models - Support integration engineers in onboarding new firms and ensuring their data aligns with Trilon's enterprise model - Work closely with cybersecurity and compliance teams to enforce data protection, retention, and access policies - Provide documentation, architecture diagrams, and operational standards for the data platform and pipelines Skills, Knowledge and Expertise - 5 or more years of experience in data engineering, data integration, or data platform development - Strong hands-on experience with Azure Data Factory, Azure Synapse, Microsoft Fabric, and related Azure data services - Proficiency in SQL, DAX, Power Query, and data modeling for Power BI - Experience designing and maintaining Power BI semantic models, datasets, and row-level security configurations - Familiarity with data governance, cataloging, and lineage management in tools like Microsoft Purview - Experience building and optimizing cloud data pipelines with structured, semi-structured, and unstructured data - Understanding of data preparation for AI and machine learning applications, including RAG architectures - Exposure to engineering and geospatial data such as CAD, BIM, and GIS - Strong analytical and problem-solving skills with a focus on scalability and performance - Excellent collaboration and communication skills across technical and business audiences - Bachelor's degree in Computer Science, Data Engineering, or related field preferred - Microsoft certifications such as Azure Data Engineer Associate or Fabric Analytics Engineer Associate are a plus - May require occasional travel to Trilon offices or partner locations for integration or collaboration activities About Trilon Trilon was formed with the vision of building the next Top 20 infrastructure consulting firm in North America by bringing together some of the nation's best infrastructure consulting firms, focused on delivering practical and sustainable infrastructure solutions. Trilon is backed by Alpine Investors, a PeopleFirst Private Equity Firm. Trilon currently comprises 5,500+ staff across the US. For more information, visit www.trilon.com. Pay Transparency The base salary range for this role is indicated in the posting. This range reflects the company's good faith estimate of the compensation for this position at the time of posting. Final compensation will be determined based on factors such as experience, skills, qualifications, internal equity, and geographic location.

• We’re looking for a Staff Data Engineer to join a multi-disciplinary engineering team building modern, enterprise-grade data platforms. • In this role, you will design scalable data lakes, warehouses, and pipelines, define governance and quality standards, and drive data platform modernization across real, in-flight work where performance, reliability, and security are critical. • You’ll mentor more junior engineers, partner with leadership on data strategy, and bring an AI-forward mindset. • Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes. • Build and optimize scalable data pipelines supporting batch and real-time processing. • Define and enforce data governance, quality standards, and compliance frameworks across the platform. • Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation. • Drive data platform modernization, optimizing for performance, cost, and scalability. • Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams. • Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows. • Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption.