• Own high-visibility FinOps and data initiatives tied to the company's most strategic KPIs. • Drive company-wide cost performance with an Excellence in the Means mindset, ensuring disciplined execution, sustainable efficiency, and measurable impact. • Lead optimization programs across Databricks workloads, pipelines, clusters, and storage to unlock scalable efficiency. • Build executive-ready dashboards and narratives that translate cost and performance into business impact. • Monitor spend in real time, detect anomalies early, and orchestrate swift cross-team action. • Define and elevate FinOps governance, standards, and accountability across domains. • Partner with engineering and platform teams to shape cost-efficient architecture decisions from day one. • Drive showback/chargeback transparency and strengthen unit-economics visibility at domain level. • Influence budget planning and forecasting with data-driven insights and ROI-based prioritization. • Convert technical findings into clear recommendations for product, finance, and leadership stakeholders. • Collaborate with Data, Finance, Product, Cloud, and Ops to accelerate measurable business outcomes.

Role Description AspenView Technology Partners is seeking an AI Data Engineer to work on a contract basis with one of our clients. You'll be reporting directly to the company executive leadership (SVP and COO), you will create visibility into operations by connecting and unifying data from a mature, federated systems landscape — with an AS/400 core — and turning it into reports, dashboards, and AI-ready data products. A central part of the role is building agentic connectors that let AI tools work safely and securely with company data, backed by disciplined management of syntax, schema, and the semantic layer. You will partner closely with the company IT team while delivering executive-level insight — helping the business move from ad hoc Excel and Claude analysis to dependable, enterprise-wide intelligence. What you will do: - Executive Reporting & Dashboards Security & Governance - Build reports and dashboards that give executive leadership clear, timely visibility into operations, working under their direction. - Translate leadership questions into well-structured, trustworthy data products. - Data Integration & Access - Connect and unify data from disparate sources across a federated landscape — including the AS/400 core — without a wholesale data-lake migration. - Reconcile mismatched data from bolt-on acquisitions (e.g., SKUs, warehousing, safety records) to maintain integrity and comparability. - Agentic Connectors & AI Enablement - Build agentic connectors that let AI tools — such as Claude — work with company data reliably. - Protect and secure those connectors: scoped access, authentication, and safe handling of sensitive data. - Schema, Syntax & Semantic Layer - Manage syntax, schema, and a semantic layer so data is consistent, well-defined, and ready for both analysts and AI. - Establish shared definitions and metrics that hold up across systems and acquisitions. - Collaboration & Delivery - Partner with AMCON’s IT team for access, cooperation, and knowledge of the existing environment. - Deliver iteratively — prioritizing the insights leadership needs most — and document what you build. Qualifications - Solid data engineering experience, with a track record of building reports and dashboards that stand up to executive scrutiny. - Practical AI fluency — comfort using tools like Claude and building and securing agentic connectors (e.g., MCP-style integrations) between AI and enterprise data. - Strong skills in syntax, schema, and semantic-layer management, and in reconciling disparate data sources. - Experience integrating data through a federated approach — querying and connecting sources — rather than relying solely on a central data lake. - Familiarity accessing data from legacy / enterprise systems, ideally AS/400 (IBM i) / DB2, via ODBC, APIs, or equivalent. - Strong SQL and a scripting language (e.g., Python), with sound data modeling fundamentals. - A security-first mindset for connectors and data access, with good judgment about sensitive information. - Excellent written and spoken English, the confidence to work directly with C-suite stakeholders, and self-directed delivery in a lean-IT environment. Nice if you have: - Hands-on AS/400 (IBM i) / DB2 data extraction and modernization experience. - BI platform experience (Power BI or similar) and semantic modeling. - MCP (Model Context Protocol) or other agentic connector / integration experience. - Background in wholesale distribution, supply chain, logistics, or retail data. - Experience unifying data after acquisitions (M&A data integration). - Data governance and security tooling experience. Equal Opportunity Employer AspenView is proud to be an equal opportunity employer. We believe in creating an environment where all employees feel welcome, valued, and empowered to succeed. We celebrate diversity and strive to build a culture of inclusion where all individuals, regardless of their race, color, gender, gender identity or expression, sexual orientation, disability, age, or any other characteristic, can thrive. We encourage applicants from all walks of life to join our team and make a lasting impact.

• Design, develop, and maintain scalable ETL/ELT pipelines to support enterprise data integration and analytics. • Ingest, transform, and integrate data from diverse sources, including flat files, JSON, XML, Excel, REST APIs, graph databases, and other structured and unstructured data formats. • Develop and optimize SQL and Python-based data processing solutions to support efficient data ingestion and transformation. • Build and maintain reusable, scalable data workflows that support business intelligence, reporting, and advanced analytics. • Load, manage, and optimize data within modern data platforms, including Databricks Unity Catalog and SQL Server Managed Instances. • Support both batch and streaming data ingestion frameworks. • Implement and maintain modern Lakehouse architecture solutions to improve scalability, performance, and accessibility. • Monitor and optimize database and pipeline performance to ensure efficient processing and storage. • Implement data quality controls to ensure the accuracy, consistency, reliability, and integrity of enterprise data. • Maintain data lineage and metadata to support governance and regulatory compliance. • Apply enterprise data management (EDM) standards and best practices throughout the data lifecycle. • Support data governance initiatives, including documentation, validation, and quality assurance activities. • Collaborate with cross-functional teams, including data analysts, software developers, architects, and business stakeholders, to understand data requirements and deliver effective solutions. • Support analytical environments focused on fraud detection, anomaly detection, financial oversight, and other data-driven initiatives. • Troubleshoot and resolve data pipeline, integration, and performance issues while continuously improving existing processes.

• Own the building, maintenance, and optimization of pipelines to ingest data from both operational databases and third-party tools into a data lake/warehouse. • Architect highly efficient ingestion patterns that handle evolving data schemas and high-volume, multi-source data streams seamlessly. • Optimize pipeline performance to ensure maximum uptime, high throughput, and cost-effective compute usage. • Use dbt to transform raw data within the data warehouse into structured, production-ready schemas. • Write templated SQL and Jinja code to enforce macro-driven, modular, and DRY (Don't Repeat Yourself) development practices. • Perform rigorous data quality checks by implementing native dbt tests. • Set up monitoring and alerting frameworks around both ingestion routines and dbt builds. • Track pipeline health metrics to measure and report on overall data freshness and platform reliability. • Build data applications that interact with the data warehouse to empower decentralized self-service analytics. • Build internal tooling and libraries to facilitate analytics and ML work.