Lead Data Engineer

Seeking a data engineer for hybrid contract role in Westmont IL. If interested , share your word document resume, work authorization, expected hourly pay rate, current location, availability for onsite work as required and interview , skill stack and LinkedIn. Westmont Illinois 60559 Shift: Monday Friday, 8:00 AM - 5:00 PM (2-3 days onsite, first few weeks maybe full onsite) Parking: no employee parking Interviews: virtual Must have : Python SQL Data Engineering (ETL/ELT) AI experience building skills/MCP Job Processing Framework What this job involves We are seeking an experienced Data Engineer on a contract basis to design and deliver core capabilities of our Leasing data platform. You will own the architecture and hands-on delivery of data models, pipelines, and integration services that consolidate leasing transaction, broker, and market availability data into a unified, governed data layer on the cloud. The ideal candidate is a full-stack data engineer with a strong delivery record across multiple large, complex projects who can operate independently, drive design decisions, and hand off well-documented, production-ready work. What you'll get to do As a Data Engineer (Contractor), you will have ownership over the design and delivery of Leasing data platform components for the duration of the engagement. Your "customers" include brokers, leasing application teams, data scientists, and analytics stakeholders who depend on trusted, well-modeled leasing data. Your mission is to modernize how leasing data is ingested, modeled, and served - including making it consumable by Agentic AI workflows and LLM-powered assistants through well-designed APIs and Model Context Protocol (MCP) integrations. This is an opportunity to deliver foundational architecture on a high-visibility platform, with clearly scoped deliverables and direct influence over design decisions from day one. Key responsibilities Data Architecture and Modeling Cloud Platform Modernization Data Engineering Delivery AI-Ready Data and MCP Integration Integration and API Development Engineering Standards and Best Practices Governance and Observability Knowledge Transfer and Handoff Who you are You are a seasoned, hands-on engineer who thrives at the intersection of technical depth and delivery. You challenge the status quo with concrete, actionable recommendations and are comfortable driving design decisions across scrum teams while remaining accountable for shipping your own work. You operate well with defined outcomes and timelines, communicate proactively with stakeholders, and take pride in leaving behind systems and documentation that others can build on. Qualifications and skills Required Qualifications 7+ years of experience in data engineering and Big Data development, including multiple large, complex project deliveries. Strong hands-on experience with cloud data platforms on Azure or AWS, including services such as Databricks, Azure Data Factory, Synapse, AWS Glue, or EMR. Advanced proficiency in Python and SQL, with deep expertise in PySpark/Spark for distributed data processing at scale. Proven expertise in data modeling and data architecture, including advanced database design and optimization across SQL (e.g., Azure SQL, PostgreSQL) and NoSQL (e.g., Cosmos DB, MongoDB) stores. Experience designing and building API services and integrating data platforms with downstream applications. Hands-on experience with LLM-driven workflows, RAG architectures, or Model Context Protocol (MCP) integrations - or demonstrable experience preparing enterprise data for AI/agentic consumption. Track record of leading workstreams and driving technical decisions across teams as a senior individual contributor. Availability to work on a contract basis with structured deliverables, and to complete thorough documentation and knowledge transfer. Experience with streaming technologies such as Kafka, Spark Streaming, or Azure Event Hubs. Familiarity with vector databases (e.g., Pinecone, Weaviate) and knowledge/graph databases (e.g., Neo4j). Experience with orchestration frameworks for LLM applications (e.g., LangChain, LlamaIndex) and advanced prompt engineering. Working knowledge of DevOps practices: CI/CD pipelines, infrastructure as code (Terraform), and containerization (Kubernetes). Exposure to commercial real estate, CRM, or transaction/deal-pipeline data domains. Experience with data governance frameworks and compliance standards (GDPR, CCPA).

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