Senior Data Engineer

Summary

Join ApartmentIQ's team and become a Senior Data Engineer, playing a pivotal role in building and maintaining the data infrastructure for Daylight, their revenue-management product. You will design, build, and own the data ingestion framework, transforming operational data into analytics-ready models for machine learning workflows. Collaborate with data scientists to ensure data trustworthiness and availability. Architect cloud-native infrastructure using Terraform, implement scalable ETL/ELT jobs in Python, and build core MLOps components. Translate business requirements into performant data solutions, working with various teams. If you enjoy end-to-end ownership and solving complex problems, this role is ideal.

Requirements

• 5+ years of software engineering experience, including 3+ years working directly with data-intensive systems, pipelines, and infrastructure
• Display a strong sense of ownership and delivering end-to-end systems — from architecture and implementation to CI/CD, observability, and infrastructure management
• Runs toward problems: has zero tolerance for bugs/issues, leans into complex issues, and proactively investigates and resolves failures
• Strong debugging capabilities; seeks root causes, not band-aids — whether it’s a data anomaly, algorithmic quirk, or system failure
• Strong Python experience — can write clear, idiomatic code and understands best practices
• Comfortable writing SQL queries to analyze relational data
• Experience with Terraform or other Infrastructure-as-Code tools for provisioning cloud-based infrastructure (e.g., AWS, GCP)
• Hands-on experience designing and implementing big data architectures, streaming or batch ETL pipelines, and understanding the trade-offs between complexity, performance, and cost
• Experience with data lakes, data warehouses, relational databases, and document stores, and when to use each

Responsibilities

• Design, build, and maintain scalable MLOps infrastructure to support model training, deployment, monitoring, and continuous integration/continuous delivery (CI/CD) of ML models
• Develop and manage robust data pipelines to extract, transform, and load (ETL/ELT) data from a variety of structured and unstructured sources
• Collaborate with data scientists and ML engineers to understand model requirements and ensure production readiness of data and model workflows
• Debug complex data issues and ML pipeline failures, collaborating closely with data scientists and ML engineers to diagnose root causes in data or algorithm behavior
• Debug data / algorithmic related problems in production for user-facing applications
• Design and optimize data storage solutions using modern data warehousing, and relational database systems
• Codify and manage cloud infrastructure using Infrastructure as Code tools, primarily Terraform, to ensure reproducibility, scalability, and auditability across environments
• Implement observability, alerting, and data quality frameworks to ensure pipeline health and uphold data integrity

Preferred Qualifications

• Math or CS background preferred and/or experience working with algorithms
• Uses LLMs and AI agents to enhance engineering productivity and explore solution spaces creatively
• Operates effectively in fast-paced, startup environments; adapts quickly and communicates clearly
• Strong collaborator and communicator, deeply integrated with the team, and proactively shares context and decisions
• Ruby and/or Ruby on Rails framework
• Writing performant code using methods like parallelism and concurrency
• AWS services: SageMaker, Lambda, Redshift, OpenSearch, Kinesis
• Experience with distributed systems

Benefits

• 100% remote across the U.S., with quarterly in-person gatherings for team offsites
• Competitive Compensation
• Flexible vacation and parental leave policies
• Medical, Dental, and Vision Insurance
• 100% paid Short-Term Disability, Long-Term Disability, and Life Insurance Program
• 401k Program