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Data Engineer (US) - Leasing

Wise Skulls Corp.Chicago, IL🇺🇸United States

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationChicago, IL, United States
MongoDBNeo4jSQLApache SparkAWSScrumDatabricksGDPRKafkaLLMPythonTerraformAzurePostgreSQLKubernetesPrompt EngineeringCI/CDAzure Data FactoryLangChainRAG

Job description

Experienced Data Engineer 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.
Overview
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: Design and deliver the data models and architecture for leasing transaction, broker, CRM, and market availability data, balancing performance, scalability, maintainability, and cost.
  • Cloud Platform Modernization: Build and optimize enterprise-grade pipelines and platform components on Azure and/or AWS (e.g., Databricks, Azure Data Factory, Synapse, Glue, EMR), migrating and consolidating siloed leasing data sources into a unified, governed cloud data layer.
  • Data Engineering Delivery: Develop robust, fault-tolerant batch and incremental data pipelines in Python/PySpark and SQL, with automated testing, monitoring, and documentation that meet production readiness standards.
  • AI-Ready Data and MCP Integration: Design data products, semantic structures, and API/MCP interfaces that make leasing data intelligently discoverable and consumable by Agentic AI systems, LLM-powered assistants, and analytics applications.
  • Integration and API Development: Design and implement integration services and APIs that expose governed leasing data to applications and downstream consumers across the enterprise.
  • Engineering Standards and Best Practices: Recommend and apply architectural standards, design patterns, and reusable frameworks; conduct design and code reviews to ensure deliverable quality across the workstream.
  • Governance and Observability: Implement data quality checks, lineage tracking, and observability aligned to platform governance standards, ensuring deliverables are auditable and compliant.
  • Knowledge Transfer and Handoff: Document architecture, designs, and operational runbooks; conduct structured knowledge transfer to client engineering teams to ensure continuity beyond the engagement.
Who you are
  • 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.
Preferred Qualifications
  • 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).
Key attributes
  • Problem-Solving Skills: Breaks down complex, ambiguous data challenges into pragmatic, well-architected solutions.
  • Ownership & Drive: Takes full accountability for deliverables from design through production handoff, without needing close supervision.
  • Technical Influence: Drives sound technical decisions across teams through evidence, prototypes, and clear architectural reasoning.
  • Communication: Explains complex technical concepts and trade-offs clearly to engineers, product partners, and stakeholders.
  • Collaboration: Works effectively with engineers, product owners, and cross-functional teams, prioritizing continuity and knowledge sharing.
  • Adaptability & Speed: Delivers high-quality results quickly within a fixed engagement window, adjusting to evolving priorities.

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