Sr Data Engineer

Accountabilities:: Lead finance-focused discovery workshops and revalidation sessions, translating dashboard wireframes and business requirements into measures, dimensions, data grain, date basis, and source requirements. Guide the development and validation of AWS Glue and Amazon Redshift data solutions in alignment with finance reporting and analytics requirements. Partner with data modelers and engineers to ensure requirements are accurately reflected in conceptual, logical, and physical data models, source-to-target mappings, and curated Redshift layers. Work closely with finance functional stakeholders to validate source data, business calculations, reporting logic, and expected results. Drive resolution of business definition gaps, source-data questions, offline or manual source dispositions, and cross-functional dependencies. Establish and maintain KPI/KSM catalogs, business glossaries, requirements artifacts, validation materials, and stakeholder sign-off documentation. Ensure finance stakeholders can effectively validate curated and semantic data layers through clear KPI definitions and structured UAT validation packs. Support UAT by explaining expected KPI results and reconciliation outcomes, identifying functional defects, and facilitating timely resolution of finance-related issues. Coordinate business sign-offs, key decisions, escalations, RAID management, and change control in collaboration with project leadership. Provide finance walkthroughs at the completion of each subject area and support knowledge transfer and handover activities. Maintain strong stakeholder engagement and alignment across business, functional, and technical teams throughout concurrent delivery waves. Manage competing priorities, dependencies, and sign-off requirements within a parallel, multi-workstream delivery environment. Requirements: 10+ years of overall professional experience in data engineering, architecture, or related technology roles. 5+ years of hands-on experience with AWS Glue and Amazon Redshift. Strong finance domain knowledge covering areas such as Accounts Payable, inventory accounting, COGS/cost accounting, cash management, cash applications, treasury, and bank reconciliation. Strong understanding of financial reporting, KPI definitions, reconciliations, accounting periods, control totals, and business validation of finance data. Experience leading finance and data discovery workshops and translating business reporting requirements into structured functional and data requirements. Experience owning requirements artifacts, KPI/KSM catalogs, business glossaries, sign-off processes, and stakeholder decisions. Experience working with data modeling, data warehousing, analytics delivery, and curated or semantic data layers. Strong understanding of data reconciliation, UAT, and validation against source-system values and expected business outcomes. Ability to collaborate effectively with finance functional consultants, data modelers, data engineers, project managers, and business stakeholders. Strong stakeholder management, facilitation, written communication, and executive communication skills. Ability to lead across multiple concurrent workstreams, manage competing priorities, and coordinate dependencies and escalations. Strong analytical and problem-solving skills, with the ability to bridge complex finance requirements and technical data solutions. Ability to work effectively in an onsite stakeholder environment and maintain alignment across business, functional, and technical teams. Benefits: $60–$65 per hour. Contract engagement running from October 1, 2026 through October 1, 2027. Remote contract opportunity with an onsite lead/stakeholder engagement component. Opportunity to lead finance-focused data transformation and analytics initiatives. Exposure to AWS Glue, Amazon Redshift, financial data modeling, and enterprise reporting. Collaboration with finance, data engineering, architecture, and project leadership teams. Opportunity to influence data quality, KPI governance, reconciliation, and business validation processes.