• Work with customer subject matter experts and Intralinks project team to identify, define, collate, document, and communicate data migration requirements • Conduct deep dive data analysis of the customer current state to validate customer requirements and define the scope of the migration • Strategize and plan the entire legacy system to new Intralinks product migration considering risks, timelines, and potential impacts • Work with the customer to map legacy data to new Intralinks product • Analyze and cleanse data where necessary • Oversee the direct migration of data, which may require unexpected adjustments to the process and schedule • Provide regular status updates to customer and Intralinks migration teams • Oversee the quality control process to ensure all data has been migrated and accounted for • Document everything from the strategies used to the exact migration processes put in place—including documenting any fixes or adjustments made • Report any issues encountered to Intralinks support • Conduct regular meetings with the product management team to prioritize and resolve issues that are critical to the success of the migration process • Develop best practices, processes, and standards to continuously improve the Intralinks data migration process • Ensure compliance with regulatory requirements and guidelines for all migrated data

Role Description As a Senior GIS Data Engineer, you will play a key role in designing, building, and optimizing Xplore’s enterprise data lakehouse platform and Machine Learning stack. This is a remote role and open to candidates based anywhere within Canada. You’ll own the end-to-end GIS data engineering function in the Data & AI team, from spatial data ingestion through publishing to ArcGIS, with primary accountability for the eCoverage platform and spatial feature pipelines that feed ML, analytics, and serviceability decisioning. You will mentor intermediate and junior engineers, contribute to platform standards, and partner with analytics and AI teams to deliver high-quality, trusted workflows. - Own and operate GIS workflows powering eCoverage qualification, including H3 indexing, framework selection, and probabilistic coverage modeling in Databricks. - Build and maintain spatial feature engineering pipelines on Databricks that supply ML models (RF propagation, churn, demand) with production-grade geospatial features. - Engineer the Databricks-to-ArcGIS publishing path, ensuring reliable, performant delivery of spatial layers into ArcGIS Online and Enterprise. - Optimize last-mile compute and cost on Databricks across Classic and Serverless, tuning Spark jobs, partitioning strategies, and spatial query performance. - Manage spatial data ingestion at scale (shapefiles, rasters, vector tiles) into Unity Catalog with governed schema and lineage. - Lead vendor engagements: scope geospatial use cases, draft data contracts and statements of work, and hold third parties to defined deliverables. - Partner with data science, analytics, and network teams to translate business requirements into spatial data products. - Implement and enforce governance, lineage, cataloging, and data quality frameworks. - Translate business requirements into scalable data engineering solutions. Qualifications - 7-10 years of experience in data engineering. - Expert-level Databricks experience, including Spark optimization, Delta Lake, and Unity Catalog governance. - Strong Python and PySpark for production data engineering. - Demonstrated GIS depth: PostGIS, spatial SQL, coordinate systems, and ArcGIS (Online and Enterprise) workflows. - Experience with H3 or comparable spatial indexing at scale. - Proven track record scoping and managing vendor data contracts and project deliverables. - Knowledge of CI/CD, infrastructure-as-code, and data observability solutions. - Strong communication skills and demonstrated mentorship experience. Preferred Qualifications - Telecom, FWA, or network coverage domain experience. - Familiarity with cloud-native open geospatial sources (DEM, building footprints, land cover). - Exposure to ML feature pipelines and probabilistic modeling. - Bachelor’s or Master’s degree in Computer Science, GIS Engineering, or related field. Condition of Employment As a condition of employment and in order to comply with industry related data security standards, this position is subject to the successful completion of a Criminal Background Check. Details will be supplied to applicants as they move through the selection process. Xplore is committed to creating an accessible environment and will accommodate disabilities during the selection process. Please let your recruiter know during the selection process of any accommodation needs.

• Design, develop, test, and maintain data solutions, applications, and data pipelines. • Participate in Agile ceremonies, backlog grooming, sprint planning, and prioritisation activities. • Contribute to the design, implementation, and maintenance of scalable, secure, compliant, and reliable data architectures. • Collaborate with data scientists, analysts, software engineers, and business stakeholders to deliver data-driven solutions. • Monitor data platform performance and implement improvements to optimise efficiency and reduce latency. • Actively ensure data quality, reliability, and operational excellence across data systems. • Evaluate and support the adoption of new data technologies, tools, and architectural approaches. • Help shape and execute the vision for Business Intelligence and Data Warehousing across the organisation. • Build cross-functional relationships with Data Scientists, Product Managers, Software Engineers, and business stakeholders to understand and meet data needs. • Promote best practices in data engineering and foster a culture of continuous improvement. • Participate in strategic planning for data initiatives, helping define objectives and execution roadmaps. • Communicate complex technical concepts and project progress to both technical and non-technical audiences. • Mentor and support team members where appropriate. • Foster a collaborative team culture built on transparency, accountability, and growth.

• Own the design and organization of the program’s data lake, including schema definitions, partitioning strategy and metadata indexing. • Design and maintain end-to-end pipelines that ingest high-bandwidth sensor logs from vehicles into cloud storage with high reliability and tolerant of ad-hoc and intermittent connectivity mechanisms. • Develop data validation and integrity checks that can detect corrupted information, missing sensors, and inconsistent calibration prior to the data being processed by downstream systems. • Implement retention, tiering and lifecycle policies for data to balance storage costs with development value. • Build tooling to query raw logs to produce curated training and evaluation datasets. • Build automation to run cost-effective pseudo-labeling workflows at the scale of data ingest. • Implement data quality and model performance metrics that are used to direct labeling effort toward the highest-value examples. • Deploy and maintain data visualization tooling to support log review, annotation QA, and autonomy debugging workflows. • Build integrations between the visualization tooling and the data lake so engineers can navigate from a dataset entry or model failure directly to the origin log data • Work with autonomy engineers to define and surface custom visualization panels and implement metrics for analyzing unstructured operating environments. • Build dashboards that provide the autonomy engineers visibility into data coverage by terrain type, operating environment and geographic region. • Establish and document data contracts between the data services and model training consumers. • Partner with perception, planning and embedded engineers across the data lifecycle: from shaping the logging schemas and collection triggers to defining the dataset interfaces that supply model training and evaluation. • Define data engineering standards, best practices, and tooling choices for an innovative and fast-paced team. • Contribute to the data roadmap and provide input to technical leadership on investment priorities. • Mentor junior engineers and raise the team’s capabilities in data infrastructure scalability and operational hygiene.