About Fasken As a premier law firm with over 900 lawyers worldwide, Fasken is where excellence meets expertise. We are dedicated to shaping the future our clients want, precisely when it matters most. For more information, visit fasken.com. Your Role Fasken is seeking a Data Engineer to join its growing Data Analytics & Engineering team. This role is ideal for a professional who is hands-on, detail-oriented, and passionate about building reliable, well-governed, and scalable data platforms. You will be responsible for designing, building, and maintaining data warehouses and lakehouses in Microsoft Fabric, developing robust data pipelines, ensuring ongoing data integrity, and supporting analytics and governance initiatives across the firm. This role requires strong collaboration skills, a governance-first mindset, and the ability to clearly communicate complex data issues to both technical and non-technical audiences. Primary Responsibilities Data Engineering & Platform Development - Design, build, and maintain data warehouses and data lakehouses in Microsoft Fabric - Develop and support ETL/ELT pipelines, dataflows, and data ingestion processes - Use notebooks (Python, SQL) to transform, validate, and move data across platforms - Build and maintain semantic models to support analytics and reporting - Ensure data integrity, accuracy, and consistency throughout all transformations Monitoring, Reliability & Optimization - Monitor daily data loads to ensure completeness, freshness, and integrity - Maintain and follow a regular operational checklist to monitor data infrastructure health - Build Power BI dashboards, reports and alerts to monitor data movement, quality, and system performance - Monitor and optimize data platform compute usage to ensure efficient performance and cost control - Troubleshoot and resolve data-related issues and business tickets in a timely manner Governance, Security & Compliance - Act as a champion and ambassador for data governance, compliance, and ethical data usage - Adhere to strict requirements such as ethical walls, access segregation, and confidentiality controls - Participate in and lead data governance initiatives, including: - Defining and standardizing data fields - Data clean-up and quality improvement efforts - Set up and maintain secure access to data resources, leveraging Microsoft Entra ID groups and role-based access control - Perform regular production system closing procedures on month end and year end. Collaboration & Communication - Collaborate closely with: - Data analysts and senior analysts - Data governance leadership - Technology teams and business stakeholders - Translate data issues and limitations into clear, business-friendly language - Support ad hoc reporting and data requests from the business - Partner with stakeholders to identify pragmatic, compliant, and scalable data solutions - Document and or update processes and Standard Operating Procedures (SOP) Engineering Practices - Experience using GitHub and DevOps practices for version control, collaboration, and deployments - Leverage Copilot and modern tooling to optimize Python and SQL code - Contribute to improving standards, documentation, and repeatable patterns across the data platform What Success Looks Like Within this role, success is measured by: - High integrity and trustworthiness of data - Reliable and stable data pipelines and platforms - Consistent and optimal time-to-delivery for data and analytics solutions - Strong adherence to governance, security, and ethical data standards - Positive feedback from business and technology partners Required Knowledge and Experience - Bachelor’s degree in math, economics, statistics, engineering, computer science, or other quantitative field, or equivalent level of experience - 4+ years of experience in data engineering or a closely related role - Strong experience with Microsoft Azure–based data platforms, including: - - Azure services, Microsoft Fabric, SQL Server - Other equivalent platforms are also considered: AWS, Snowflake - Proficiency in SQL and Python for data transformation and pipeline development - Experience building and maintaining: - - Data warehouses and/or lakehouses - Data pipelines and dataflows - Familiarity with Power BI and semantic modeling (sufficient to build and support functional reports) - Experience working with data governance, access control, and compliance requirements - Strong troubleshooting, communication, and collaboration skills - Microsoft Certification required (or candidate to obtain Certification within the first year) - Knowledge of the Elite 3E ERP is an asset - Strong analytical and problem-solving skills; - Ability to work in a Remote-first environment, where internal clients may be located in various geographical locations and timezones. - Excellent planning and organizational skills, ability to manage multiple files simultaneously and meet established deadlines; - Professionalism, discretion and commitment to outstanding customer service; - Autonomy and ability to deal with multiple stakeholders; - Organizational skills, thoroughness and ability to manage priorities; Pay Range $90,000 to $110,000 per annum. Pay range to be commensurate with experience. Vacancy This role is being posted to fill a current vacancy.

• Help with optimization, support and deployment of cloud-based data systems • Build secure, scalable, and compliant database solutions using Azure SQL Server and other cloud-native technologies • Support the evolution of analytics lakehouse and all of its data sources • Ensure compliance with Equal Employment Opportunity laws, HIPAA, ERISA and other regulations

This position will be involved in: • Implement integration of large, diverse, ERP data sets into our enterprise data lake and data warehouse, in conformance with enterprise architecture and design patterns • Design, document, and develop the architecture of data projects in IT, including data flow diagrams, data models, logical & physical solution architectures, data dictionaries, data maps and data catalogs. Collaborate with both business and IT users • Collaborate & work closely with analysts, stakeholders and cross-functional teams • Innovate & adapt new technologies to improve our data infrastructure • Optimize existing processes for performance and scalability • Implement & Monitor enterprise data quality checks and audits • Takes ownership of problems and proactively resolves technical problems, ensuring that technical solutions continue to meet business requirements and aligns with the vision, mission, objectives, strategy of the team highly motivated, goal driven, innovative, curious and open minded, Fun to work with, team player #LI-P1 - #LI-PR1 We are looking for someone with the following experience: • 5+ years of relevant experience in a data engineering role with a focus on building data integration solutions in an enterprise environment • 5+ years of experience with a standard integration tool (SSIS, Informatica, Jitterbit, etc.) • Experience working with ERP data (Dynamics AX, SAP, Oracle Financials, etc.) • Qualification in Computer Science or Information Systems, Engineering or related field is preferred • Expert level skills in SQL and other Snowflake relevant languages such as Python • Strong understanding of ETL concepts, Data warehousing concepts, GIT and CI/CD practices • Experience with cloud data platforms (e.g., AWS, GCP, Azure) and their data services. Snowflake experience preferred • Excellent problem-solving skills and attention to detail • Experience with common modern ingestion tools • Experience with visualization tools like PowerBI or equivalent • Strong communication and collaboration skills, with the ability to work effectively in a remote team environment • Ability to provide technical leadership experience to junior team members WHO IS VONTIER Vontier (NYSE: VNT) is a global technology company powering the way the world moves. We empower businesses in the transport sector to adapt to a fast-changing landscape by uniting productivity, automation and multi-energy technologies. Our smart, connected solutions serve roadside convenience retail stores, fleet operators, and auto repair technicians. From integrated payments and EV charging software to carwash technology and retail automation, we help customers stay productive and prepared for a rapidly evolving industry. With decades of expertise and a balanced portfolio, Vontier enables businesses to navigate complexity, unlock growth, and build a cleaner, safer future. Driven by continuous improvement and the dedication of Team Vontier, we empower businesses to think bigger, act boldly, and thrive on the road ahead. Learn more at www.vontier.com #LI-P1 - #LI-PR1 -

• Define and own the enterprise data architecture strategy, including conceptual, logical, and physical data models across the organization's core domains • Establish and govern data standards, naming conventions, schema design principles, and modeling best practices used by Data Engineering and Analytics Engineering teams • Lead the design of scalable, reusable data products in the semantic and analytical layers, ensuring consistency across Decision Science, Data Science, and self-service consumption • Partner with the Product Data team to align on shared architectural standards, data contracts, and platform decisions—acting as a peer and collaborator, not a dependency • Evaluate and advise on data platform and tooling decisions (cloud data warehouses, lakehouse patterns, orchestration, metadata management, cataloging) • Identify and resolve architectural gaps, redundancy, and data quality risks across the data estate • Contribute to—and in many cases lead—the development of a business glossary, data catalog, and enterprise ontology for key data domains • Act as a senior advisor to Data Science on data availability, feature engineering infrastructure, and model data requirements • Collaborate with Decision Science leadership to ensure analytical data models are structured for performance, clarity, and governed self-service • Champion data governance, lineage, and observability as first-class architectural concerns • Mentor and guide engineers and analytics engineers on architectural patterns and data modeling best practices