Data Architect - Data Engineering Lead | Minnetonka, Minnesota | Remote

Data Architect / Data Engineering LeadWe are seeking a Data Architect / Data Engineering Lead to own the end-to-end architecture and engineering standards for modern data platforms. This role partners closely with product, analytics, and engineering teams to design scalable, secure, and cost-effective data solutions - while remaining hands-on with implementation where it matters most. This position blends data architecture leadership (roadmaps, standards, patterns, governance) with data engineering execution (pipelines, modeling, orchestration, performance optimization), ensuring data is delivered reliably and with high quality.Primary Responsibilities:Define and evolve target-state data architecture, including reference architectures, integration patterns, and platform standards across warehouse/lakehouse ecosystemsLead architectural decision-making for cloud data platforms (e.g., Snowflake, Databricks) including compute/storage patterns, multi-environment strategies, security boundaries, and cost controlsCreate technical roadmaps that align business outcomes with scalable data capabilities (analytics, operational reporting, AI/ML readiness)Own enterprise and domain data modeling standards (conceptual/logical/physical), including dimensional modeling, canonical models, and patterns for curated datasetsEnsure consistent definitions, metrics alignment, and high-quality, analytics-ready data productsLead design and build of robust ELT/ETL pipelines using Python and distributed processing (Spark) as neededDrive performance tuning and optimization across pipelines and data platforms (query patterns, clustering/partitioning, incremental loads, caching strategies)Establish engineering practices that improve reliability, maintainability, and developer productivityDesign and operationalize workflow orchestration using Apache Airflow, including DAG standards, scheduling patterns, dependency management, retries, SLAs, and backfillsImplement and manage Airflow runtime patterns in managed platforms such as Astronomer (or equivalent), including environment promotion strategies and operational readinessDefine and implement data quality frameworks (validation checks, anomaly detection, reconciliation, data contracts) and operational monitoringPartner with governance/security stakeholders to ensure compliant data handling, access controls, lineage, metadata, and auditabilityLead/mentor engineers and architects through design reviews, code reviews, and architecture governance forums; raise the bar on engineering excellenceTranslate complex technical concepts into clear guidance for stakeholders; drive alignment on tradeoffs, timelines, and expected outcomesYou'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.Required Qualifications:8+ years in data engineering and/or data architecture roles with demonstrated ownership of enterprise-scale data solutions6+ years of data architecture experience: designing modern data platforms, integration patterns, and scalable data delivery approaches3+ years of data modeling expertise (dimensional, normalized, canonical models; conceptual/logical/physical modeling)3+ years of proficiency with Python and SQL; solid engineering discipline (testing, documentation, maintainability)1+ years of experience with cloud data technologies including Snowflake and/or Databricks (Spark-based engineering)1+ years of workflow orchestration experience with Apache Airflow; experience with managed Airflow platforms such as Astronomer1+ years of experience implementing production-grade pipelines (batch and/or streaming), including CI/CD practices, environment promotion, and operational support1+ years of experience leading technical direction, mentoring engineers, and influencing architecture decisions across teamsPreferred Qualifications:Bachelor's degree in Information Technology, Computer Science or related fielddbt experience (modeling and transformation lifecycle management)Experience with Snowpark frameworkExperience with data governance/metadata tools and practices (catalog, lineage, access policies)Observability tooling experience (pipeline monitoring, alerting, logging, operational dashboards)Infrastructure-as-code and containerization (Terraform, Docker, Kubernetes) in support of data platformsExperience supporting regulated data environments (e.g., healthcare/PII/PHI) and security-by-design patterns