Data Engineer

Whisker•Auburn Hills, MI

26d•Hybrid

About The Position

Whisker is redefining what it means to live with cats—designing intelligent systems that remove friction, elevate the everyday, and celebrate the quiet brilliance of feline companionship. Today, Litter-Robot leads the category. Tomorrow, an entire ecosystem that expands what’s possible for cats and the people who love them. We believe the future is feline. And we’re imagining that future today. We work onsite 4+ days a week, with our team based in Auburn Hills, Michigan, and Juneau, Wisconsin. Our team of 700+ passionate pet people thrives on collaboration, innovation, and the occasional office cameo from a four-legged friend. What You’ll Do: We are seeking a Senior Data Engineer / Data Engineer with strong hands-on Databricks experience to design, build, and optimize scalable data pipelines and platforms that power analytics and decision-making across the organization. This role partners closely with data science, analytics, and engineering teams to deliver reliable, high-performance data solutions in a modern cloud lakehouse environment. Summary: The Senior Data Engineer / Data Engineer is responsible for designing, developing, and maintaining scalable data pipelines and infrastructure using Databricks and related cloud technologies. This role ensures data is reliable, secure, and readily accessible to support analytics, reporting, and machine learning initiatives.

Requirements

BA/BS in Computer Science, Software Engineering, or a related technical field, and/or equivalent years of experience
8+ years of professional software engineering experience, including 3+ years directly managing engineers with ownership of hiring, performance, and career development
Demonstrated success leading backend service teams at consumer scale with distributed services and APIs supporting a large installed base of application users or devices
Expertise in API design and service architecture, including versioning and backward compatibility for clients that cannot all be upgraded at once
Experience with high-volume data ingestion and event-driven architecture (Kafka, Kinesis, or equivalent), including the pipelines that feed analytics and machine learning
A track record of driving measurable reliability improvement — fewer or less severe incidents, improved uptime, a healthier on-call practice, or materially more stable deployments
Experience leading distributed teams across multiple time zones, including blended teams of full-time employees and contractors or outsourced partners
Experience inheriting an existing team and improving retention, role clarity, and delivery predictability
Proven ability to recruit, level, and develop engineers, including promoting individual contributors into senior and lead roles
Current technical credibility: able to review code, lead architecture discussions, and prototype solutions independently.
Working experience with AWS infrastructure and the ability to own cloud architecture, cost, and capacity decisions for the services their team runs.
Familiarity with platform engineering practice such as infrastructure as code, CI/CD pipelines, containerization or serverless deployment models, and observability tooling sufficient to set direction and hold quality standards
Ability to work a hybrid schedule based out of Whisker's Auburn Hills, Michigan headquarters
Maintains confidentiality of proprietary information
Ability to maintain a professional atmosphere in a fast-paced environment with numerous interactions and interruptions
A high degree of initiative, self-motivation, and ability to motivate others
Ability to establish and maintain cooperative working relationships with Team Members and colleagues
Comfortable with office pets (cats, dogs)

Nice To Haves

Strongly preferred experience with IoT or connected-device platforms, device telemetry, fleet management, over-the-air updates, MQTT or similar protocols
Background at a consumer hardware or connected-product company where software ships alongside physical product
Experience managing the firmware-to-cloud boundary and hardware-dependent release trains, including field failure analysis
Deeper platform or SRE background such as Kubernetes, ECS, Terraform, or equivalent at production scale
Experience with GraphQL and API gateway patterns
Experience with time-series or purpose-built IoT data stores
Experience transitioning a contractor-heavy team toward a stronger full-time core without disrupting delivery
Experience in a direct-to-consumer or subscription business
Prior Director of Engineering experience is welcome — this role owns a broad scope across several service domains

Responsibilities

Designs, builds and maintains scalable ETL/ELT pipelines using Databricks, Apache Spark, and Delta Lake
Develops and optimizes data workflows for batch and streaming data processing
Architects and implements data lakehouse solutions following medallion architecture (bronze/silver/gold layers using DBT)
Collaborates with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions
Writes efficient, well-documented PySpark/SQL code for data transformation and processing
Develops and supports MLOps workflows, including model tracking, versioning, and lifecycle management using MLflow
Builds and maintains CI/CD pipelines for both data engineering and machine learning model deployment
Implements monitoring and alerting for data pipelines and ML models to proactively identify and troubleshoot performance issues, data drift, and failures
Monitors, troubleshoots, and optimizes pipeline and model performance, resource utilization, and cost efficiency within Databricks
Ensures data quality, integrity, and governance across all pipelines and datasets
Manages and optimizes Databricks clusters, jobs, and workflows
Integrates Databricks with cloud platforms (Azure, AWS, or GCP) and other enterprise systems
Implements data security best practices, including access controls and data masking
Mentors junior data engineers and provide technical guidance on best practices, including MLOps standards
Participates in architecture reviews and contribute to the evolution of the data and ML platform strategy
Documents technical designs, data models, pipeline processes, and ML deployment workflows
Stays current with emerging tools, technologies, and best practices in data engineering and MLOps
Performs additional responsibilities when required