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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Data Engineer - **Company:** Carhartt - **Location:** Dearborn, MI, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Automation of Tests, Microsoft Azure, Code Review, Information Systems, System Configuration, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Data Warehousing, Distributed Computing Environment, Python (Programming Language), Machine Learning, Metadata, Operational Databases, Performance Tuning, Query Optimization, Cloud Services, Azure Data Lake, SAP (Applications), Software Engineering, SQL Databases, Data Streaming, Data Logging, Azure Data Factory, Sql Optimization, Software Troubleshooting, Generative AI, Git, Data Layers, Data Lakes, Pyspark, Information Technology, Deployment Automation, Data Management, Physical Data Models, Software Version Control, Data Pipelines, Serverless Computing, Databricks - **Published:** October 18, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=29728476de052cf8 ## About the Role * Bachelor's Degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field or equivalent related experience. * Graduate degree preferred., * Minimum of 8 years of professional experience in data engineering, data warehousing, business intelligence, cloud data platforms, or a related field. * Demonstrated experience designing, implementing, and supporting enterprise data solutions using Azure Databricks and Lakehouse architecture. * Advanced experience with data pipeline design and orchestration, including batch and streaming patterns, dependencies, scheduling, monitoring, and recovery. * Advanced SQL skills, including data modeling, query optimization, performance troubleshooting, and complex analytical transformations. * Professional experience with Python and distributed data processing frameworks such as PySpark. * Experience with Azure Databricks, Delta Lake, Azure Data Lake Storage (ADLS), Azure Data Factory (ADF), Unity Catalog, Azure DevOps, and related Microsoft Azure data platform services. * Experience designing conceptual, logical, dimensional, relational, and physical data models for analytical and operational use. * Experience optimizing cloud workloads for performance, scalability, reliability, and cost effectiveness. * Working knowledge of data governance, metadata, cataloging, lineage, data quality, security, privacy, retention, and access-control principles. * Experience preparing trusted, governed, reusable, and well-documented data products that enable reporting, advanced analytics, data science, machine learning, generative AI, and future AI-driven business capabilities. * Experience with Git-based development, code reviews, automated testing, CI/CD, and deployment automation. * Experience integrating data from SAP, SAP Datasphere, APIs, files, events, product and master data platforms, and third-party enterprise systems. * Demonstrated technical leadership through architecture and solution design, engineering standards and best practices, design reviews, mentoring, code reviews, reusable patterns, and technical guidance across delivery teams. * Excellent verbal and written communication skills, including the ability to explain technical designs, risks, decisions, and tradeoffs to technical and business audiences. * Strong cross-functional collaboration skills with business partners, analysts, data scientists, product owners, architects, security teams, and application teams. * Ability to isolate complex problems, evaluate options, make practical decisions, and provide advanced production support during critical incidents. * Professional cloud or data engineering certification considered a plus. ## Description The primary responsibility of this position is to design, build, optimize, and support secure, reliable, and scalable enterprise data solutions. The Senior Data Engineer provides technical leadership for Azure Databricks and Lakehouse architecture, data pipeline design and orchestration, data modeling and performance optimization, cloud-native data platforms, data governance and quality, and AI/ML-ready data products. This role mentors engineers and collaborates across business, analytics, architecture, security, and application teams to deliver trusted data for reporting, advanced analytics, machine learning, and operational use., * Design, develop, test, deploy, and support scalable batch and streaming data pipelines using Azure Databricks, Azure Data Factory, Python, PySpark, SQL, and related cloud-native services. * Design and evolve Lakehouse architecture using Delta Lake and Azure Data Lake Storage, including reusable data products and medallion-oriented data layers where appropriate. * Create and optimize conceptual, logical, dimensional, relational, and physical data models that are aligned with business meaning and analytical requirements. * Optimize pipelines, SQL queries, clusters, storage layouts, and processing patterns for performance, reliability, scalability, and cost effectiveness. * Implement orchestration, dependency management, scheduling, monitoring, logging, alerting, error handling, recovery, lineage, and observability for production data workloads. * Apply data governance and quality practices, including metadata, cataloging, validation, reconciliation, security, privacy, retention, and access controls. * Engineer trusted, well-documented, and reusable datasets and features that support reporting, advanced analytics, data science, and AI/ML use cases. * Lead technical design discussions, establish engineering standards and reusable patterns, conduct code and design reviews, and document architecture decisions and tradeoffs. * Mentor data engineers through pairing, technical guidance, code reviews, troubleshooting, coaching, and clear documentation. * Partner with business stakeholders, analysts, data scientists, product owners, architects, security teams, application teams, and vendors throughout the solution life cycle. * Apply software engineering practices including Git-based source control, automated testing, CI/CD, deployment automation, and infrastructure configuration. * Provide advanced production support, root-cause analysis, corrective actions, and continuous improvement for complex data platform and pipeline incidents.