
Data Engineer
Empire State Realty Trust
full-time
Posted on:
Location Type: Hybrid
Location: New York City • New York • United States
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Salary
💰 $135,000 - $145,000 per year
About the role
- Design, build, and deploy AI-driven solutions using Python, APIs, and enterprise platforms (e.g., ChatGPT, Agentforce, internal automation tools, Agentic AI Assistance)
- Partner with business and analytics teams to identify and prioritize automation opportunities that streamline workflows and enhance decision-making
- Integrate AI capabilities into existing enterprise systems and applications to improve operational efficiency
- Develop, maintain, and optimize data pipeline, ETL workflows and integrations using Informatica, Azure Services, Microsoft Fabric, and Semantic Models for Power BI to ensure accurate, timely, and reliable data ingestion and transformation
- Manage the enterprise Data Lake Architecture to support analytics, reporting, and AI model training, including ingestion, transformation, and overall data management
- Build and maintain integrations between applications, databases, and cloud platforms using APIs, middleware, and native connectors (e.g., REST/GraphQL API)
- Curate and publish high-quality datasets for analytics and visualization tools such as Power BI, including developing and maintaining semantic models and enterprise data schemas
- Maintain clear and up-to-date documentation for technical specifications, data flows, integration architecture, and AI models
- Enforce compliance with data security, access control, and governance frameworks
Requirements
- Bachelor’s degree in computer science, data engineering, data science, information systems, or related fields
- 5–8 years of experience in data integration, analytics, or related fields 5+ years of ticketing / attraction focused experience
- Proven experience managing data pipelines, AI integrations, and enterprise analytics environments
- Exposure to real estate, REIT, or property technology environments preferred
- Strong proficiency in Informatica, SQL, Python, and Power BI
- Experience with AI/ML frameworks, LLM-based tools, and cloud platforms (e.g., Azure, AWS, or GCP)
- Hands-on experience developing and maintaining API integrations
- Strong understanding of data governance, lineage, and security best practices
- Excellent analytical, organizational, and communication skills
- Ability to collaborate across teams and translate technical concepts into business value
- Deep expertise in enterprise architecture, complex application design, AI integration, advanced analytics, and emerging technologies to deliver scalable, secure, and innovative solutions
- Strong organizational skills: ability to prioritize work and meet deadlines in a fast-paced environment
- Excellent written and verbal communication skills, adept at conveying complex technical concepts to diverse audiences and building trust with both technical and non-technical stakeholders
- Commitment to ethical, secure, and responsible data and AI practices.
Benefits
- Competitive base salary and bonus
- Health/Dental/Vision insurance
- Company sponsored Life, AD&D, STD (with Salary Continuation), and LTD Insurance
- Voluntary Enhanced LTD Program
- Voluntary Hospital, Accident, and Cancer Programs
- 401(k) with 100% match up to 5%
- Paid parental leave
- Pre-tax transit accounts
- Employee Assistance Program for emotional, financial, and legal support
- Generous paid time off
- Flex remote work time
- Flex Summer Fridays
- Employee engagement programs
- Volunteer time off
- Continuing education
- Complimentary Empire State Building Observatory access
- Complimentary gym membership and other wellness benefits
- Employee Discount Programs
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonAPIsInformaticaSQLPower BIETL workflowsdata pipeline managementAI integrationsdata governancedata transformation
Soft Skills
analytical skillsorganizational skillscommunication skillscollaborationability to prioritizetranslating technical conceptsbuilding trustethical data practicesresponsible AI practices