Data Engineer

AIPPY Technologies - Intelligence Behind Smart Bridging the digital and physical worlds. AIPPY Technologies helps organizations design and implement digital, data, IoT, and AI strategies that transform how they innovate, operate, and scale. We specialize in enabling the next generation of smart manufacturing, transportation, and infrastructure by bringing digital intelligence to the core of real‑world systems. At AIPPY, we combine deep industry expertise with advanced technology capabilities to help organizations modernize operations, unlock real‑time insights, and build resilient, data‑driven ecosystems. From strategy through execution, we guide our clients in adopting the platforms, architectures, and AI‑driven models needed to accelerate innovation and deliver measurable business impact - not experiments, not hype. We don’t just build AI. We change how the business runs. Role Description (Contract-to-hire, 1099, W2) Overview AIPPY Technologies is seeking a highly skilled data engineer to serve as a strategic consultant supporting its client's enterprise data modernization and AI initiatives. The data engineer is responsible for designing, developing, and maintaining scalable data pipelines that integrate data from enterprise systems, including Dynamics 365 Finance & Operations (F&O), Dynamics 365 Customer Engagement (CE), Enterprise Finance Management, and other critical business applications into enterprise data and analytics platforms. This role focuses on hands‑on engineering, optimization, and support of data integration, transformation, and data modeling solutions that enable enterprise reporting, analytics, and AI‑driven initiatives. Working closely with Enterprise Data Architects, business stakeholders, and cross-functional teams, the Data Engineer translates architectural designs into scalable, cloud‑native solutions using modern data platform technologies such as Synapse Analytics, Azure Databricks, Microsoft Fabric, etc. The role supports the implementation of lakehouse and medallion architecture patterns, ensures data quality and reliability, and contributes to building a trusted, AI‑ready data foundation that powers enterprise analytics, operational reporting, self‑service BI, and emerging AI and machine learning use cases. Principal Duties and Responsibilities (Essential Functions) Implement modern data warehouse architecture patterns, including lakehouse and medallion architectures (raw, curated, consumption) that are scalable, performant, and maintainable. Integrate data from multiple systems, including D365 F&O/CE, SAP, Infor XA, and other ERP systems into unified data models that support enterprise reporting, analytics, and AI. Design, build, and optimize scalable data pipelines that support batch, near‑real‑time, and real‑time data integration patterns. Measure and monitor performance and cost of data pipelines, ensuring solutions meet SLAs for data availability, quality, refresh performance, and efficiency. Assemble, transform, and manage large, complex datasets, ensuring consistency, reliability, and usability across business domains. Ensure data quality is embedded in pipelines by implementing validation, monitoring, alerting, and error‑handling mechanisms to support trusted downstream consumption. Build and maintain data integration frameworks using Azure services such as Synapse Analytics, Fabric, and Databricks, along with SQL, Python, and Spark‑based processing. Identify and implement process improvements to increase scalability, automate manual processes, and optimize data delivery. Develop analytical datasets that enable actionable insights into business performance, operational efficiency, and customer metrics. Collaborate with Data Architects, data & AI engineers, analytics teams, and business stakeholders to ensure solutions align with business requirements and architectural standards. Create and maintain technical documentation, including data mappings, pipeline designs, and operational runbooks. Contribute to testing, QA, and validation of data pipelines and systems to ensure reliability and accuracy. Support building an AI‑ready data foundation by: Delivering clean, structured, and well‑documented datasets Enabling efficient data access patterns for advanced analytics Supporting downstream use cases such as exploratory analysis and model consumption Ensuring compliance with security, privacy, and enterprise standards in all data solutions. Job Specifications Strong understanding of data engineering best practices and modern cloud data platforms. Expertise in data modeling, including relational, dimensional, and lakehouse‑based approaches. Ability to translate business requirements into technical data solutions. Strong collaboration and communication skills across technical and business teams. Ability to manage multiple concurrent priorities in a fast‑paced environment. Experience debugging and resolving complex data pipeline and performance issues. Strong problem‑solving mindset with ownership and accountability. Required Education and Experience Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent experience. 3–5+ years of experience in data engineering or analytics development in a cloud environment. Strong experience with ETL/ELT pipeline development and data integration using data integration tools like Databricks Notebook, Talend, Informatica, or Boomi. Hands‑on experience with Azure‑based data platforms, including: Microsoft Fabric Advanced proficiency in SQL and experience with Python (or similar languages). Experience working with Spark, Pandas, or distributed data processing frameworks. Experience working in Agile and DevOps environments, including CI/CD practices and source control. Experience building data solutions that support modern analytics and AI‑ready data ecosystems. Preferred Experience Manufacturing industry experience in complex data environments. Experience with Microsoft Dynamics 365 F&O and D365 CE data integration and analytics. Experience supporting advanced analytics or AI/ML workloads through well‑engineered data pipelines. Understanding of patterns that enable AI‑ready platforms, including master data management, data governance frameworks, and trusted data products that support analytics, machine learning, and generative AI use cases. Experience integrating ERP and CRM systems into enterprise analytics platforms. Note: This is a contract‑to‑hire position with an opportunity to convert to full‑time in 6 months. #J-18808-Ljbffr