Senior Data Engineer - Remote Opportunity!

Futures start here. Where first steps, new friendships, and confident learners are born. At KinderCare Learning Companies, the first and only early childhood education provider recognized with theGallup Exceptional Workplace Award, we offer a variety of early education and child care options for families. Whether it’s KinderCare Learning Centers, Champions, or Crème de la Crème, we build confidence for kids, families, and the future we share. And we want you to join us in shaping it—in neighborhoods, at work, and in schools nationwide.At KinderCare Learning Companies, you’ll use your skills and expertise to support the work (and fun) that happens in our sites and centers every day. From marketers and strategists to financial analysts and data engineers, and so much more, we’re all passionate about crafting a world where children, families, and organizations can thrive.As Senior Databricks Engineer, you will be a hands-on technical expert and force multiplier on our Databricks-based data platform. You’ll own the design, optimization, and governance of our medallion lakehouse architecture (Bronze/Silver/Gold), the Unity Catalog, and the pipelines that feed enterprise BI and emerging AI/ML products. You'll operate deep in the Databricks ecosystem daily (Delta Lake, Unity Catalog, Workflows, Delta Live Tables / Lakeflow, MLflow) while also shaping how the platform supports next-generation capabilities, including Databricks-native ML/AI features such as Genie spaces, feature engineering, model serving, and vector search. This role partners closely with the Data Engineering Lead and BI Architect and sits in a SOX-governed environment, so a strong instinct for data governance, access control, and auditable engineering practices is essential.Responsibilities:Platform & Pipeline Engineering Design, build, and optimize production-grade ETL/ELT pipelines across the medallion architecture using Delta Lake, Delta Live Tables / Lakeflow Declarative Pipelines, and Databricks Workflows Own performance tuning and cost efficiency across the platform — cluster/job sizing, Photon, partitioning and Z-ordering, Liquid Clustering, Auto Loader, and DBU cost governance Architect and enforce data models that support enterprise BI (Microsoft Fabric/Power BI) and downstream analytics products Build and maintain CI/CD pipelines for Databricks assets (Databricks Asset Bundles, Repos, Git-based deployment) following Agile and Test-Driven Development practices Integrate platform pipelines with middleware and source systems (e.g., Boomi) and cloud-native services Governance, Security & Reliability Administer and evolve Unity Catalog: catalogs/schemas, fine-grained access control, lineage, row/column-level security, and workspace-catalog bindings Implement and enforce data quality, observability, and reliability practices (expectations/constraints, monitoring, alerting, SLA management) across pipelines Partner with security, compliance, and audit teams to maintain SOX ITGC alignment; access reviews, change control, and auditable engineering practices Troubleshoot and resolve complex production data pipeline issues, performing root-cause analysis and implementing preventive fixes Create clear, durable documentation of architecture, procedures, and operational runbooks Forward-Looking ML/AI Enablement Evaluate, pilot, and productionize Databricks-native AI/ML capabilities — including Genie for natural-language data access and MLflow for experiment tracking and model lifecycle management, Feature Store, and Model Serving Support the build-out of vector search and retrieval-augmented generation (RAG) patterns on top of governed Unity Catalog data for internal AI use cases Collaborate with data science and analytics stakeholders to prepare curated, ML-ready Gold-layer datasets and feature pipelines Stay current on the Databricks roadmap (Lakehouse AI, Mosaic AI, Agent frameworks) and recommend adoption where it advances platform maturity and business value Help define guardrails and human-in-the-loop controls for AI-assisted and agentic engineering workflows introduced to the platform Collaboration & Leadership Serve as a technical mentor to mid-level data engineers, raising the bar on Databricks best practices, code quality, and architectural rigor Partner with the Data Architect and Data Engineering Lead to translate business requirements into technical specifications for BI and AI products Collaborate cross-functionally with technical and non-technical stakeholders, including product, security, and business teams Contribute to data governance, data security, and data privacy standards across the platform Qualifications: Bachelor’s degree in computer science, information systems, engineering, statistics, or related field or equivalent work experience 7+ years of experience as a data engineer with 3+ years specifically architecting and operating production workloads on Databricks Strong understanding of data governance, data security, and acces