Value Data Engineer

The Value Data Engineer sits within Logile's Customer Experience (CX) organization and provides the data engineering and analytics backbone behind every value conversation Logile has with a prospect or customer — from early-stage opportunity sizing during the sales cycle, through detailed business-case modelling, to evidenced ROI after go-live. Logile does not run a separate Value Realization function, so this role is CX's custodian of value data: it owns the data, models and evidence base that make every value claim credible, working closely with the Account Manager and Customer Success Manager to ensure it is grounded in real customer and prospect data, consistent with Logile's Account Management Charter and the AM & CX partnership model.

Pre-sale opportunity sizing: Gather publicly available data on prospects (sector, scale, format, geography), using AI-assisted research and data-enrichment tools to do this efficiently and at scale, and run early-stage, directional value calculations tied to the financial and business pressures Logile's platform addresses, to support the sales and pre-sales process. Deeper value case development: Use prospect-provided data to develop a deeper, defensible value case, including detailed payback timelines, establishing and maintaining Logile's value methodology, workshops, baselines and business-case standards as CX's custodian of value data. Operational value insight: Interrogate live customer system data to surface value opportunities and operational insight — including forecast accuracy, schedule effectiveness, labor productivity, production output and waste reduction. Usage and adoption insight: Interrogate platform usage data to assess whether customers are using the full breadth of Logile's functionality, and using it effectively, feeding this into the CSM's adoption and customer health view. Post-launch value realization: Partner directly with customers, alongside the CSM, to measure and evidence value realized against the original business case after go-live — supporting QBR reporting, renewal readiness and expansion conversations. Key Relationships The role is embedded in CX and works cross-functionally in support of the Account Management Charter's "one aligned customer narrative": Relationship How they work together Cadence Account Manager (incl. Pre-Sales) Receives early-stage, directional value sizing to support pre-sales qualification, plus the evidenced value and ROI data that underpins renewal readiness, commercial risk visibility and expansion narratives. Per opportunity / QBR / renewal cycle Customer Success Manager Supplies adoption, usage and operational-value data that feeds the CSM's customer health view, business reviews and expansion conversations. Ongoing / weekly Customer Support Manager The role's main connection into the Product team: cross-references ticket and incident patterns with usage and value data, and channels pattern-level insight into Product's roadmap conversations via the Customer Support Manager; supports QBR support inputs. As needed / regular review Skills & Experience Bachelor's degree in computer science, engineering, data analytics, or commensurate work experience. 5-8 years of experience as a Data Engineer, Analytics Engineer, or similar role, ideally with some exposure to commercial, financial or value-analytics work. Strong SQL and Python/R; experience building and maintaining ETL/ELT pipelines against large structured and unstructured datasets. Writing scripts for statistical analysis, handling API requests from customer platforms, or executing predictive models.