Sr. Data Engineer

Carhartt•Dearborn, MI

5d•Remote

About The Position

The Sr. Data Engineer designs, builds, optimizes, and supports secure, reliable, scalable enterprise data solutions. The role provides technical leadership in 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. The position 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. The position is remote, with no regular requirement to work onsite; travel onsite is limited to special events. The work takes place in a normal office environment and requires extended periods of sitting, standing, and typing on a computer. Some weekend work may be necessary.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related technical field, or equivalent related experience.
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 for 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.

Nice To Haves

Graduate degree preferred.
Professional cloud or data engineering certification considered a plus.

Responsibilities

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 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, reusable datasets and features for 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.