Cloud Data Engineer Jobs
Cloud Data Engineer jobs are open across fintech, healthcare, retail, and media at every level from junior to staff and principal, with specializations in pipeline architecture, lakehouse design, and real-time streaming. Find a role that fits from the openings below and apply directly.
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The Senior Cloud Data Engineer designs, develops, and optimizes scalable data solutions that support manufacturing operations, business intelligence, advanced analytics, and digital transformation. This role builds and maintains cloud-based data pipelines using Azure Databricks and related Azure services to integrate enterprise operational and business data. The ideal candidate combines hands-on cloud data engineering experience with collaboration across business stakeholders, analysts, architects, and data engineers to deliver reliable, secure, and high-performing data products that enable data-driven decisions across the organization.
Essential Functions and Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines using Azure Databricks, PySpark, Python, and SQL.
- Build and optimize ingestion frameworks for ERP, MES, Industrial IoT, quality, supply chain, inventory, finance, and related operational systems.
- Develop reusable engineering patterns and standards that improve delivery speed, supportability, and reliability.
- Implement and support lakehouse solutions using Delta Lake and Medallion Architecture across Bronze, Silver, and Gold layers.
- Design and optimize data models for reporting, analytics, AI, and machine learning use cases.
- Maintain data lineage, metadata, technical documentation, and operational runbooks.
- Integrate Azure Databricks with Azure Data Factory, Azure Data Lake Storage Gen2, Azure Key Vault, Microsoft Fabric, Power BI, and Azure DevOps.
- Support orchestration and movement of data between enterprise applications and cloud platforms.
- Monitor and optimize Databricks clusters, jobs, notebooks, and workflows for performance, scalability, and cost efficiency.
- Troubleshoot production issues, identify root causes, and implement sustainable corrective actions.
- Contribute to monitoring, alerting, testing, and operational support practices.
- Apply organizational standards for data quality, governance, privacy, security, and compliance.
- Implement role-based access controls and governed data access using Unity Catalog and Azure security services.
- Build automated validation, auditing, and data quality controls into engineering workflows.
- Partner with business users, analysts, architects, and IT teams to translate requirements into maintainable technical solutions.
- Participate in Agile ceremonies, sprint planning, code reviews, and technical design reviews.
- Mentor junior engineers and promote coding, testing, documentation, and deployment best practices.
- Bachelor's degree in computer science, information systems, data engineering, or a related field; an equivalent combination of education and experience may be considered.
- 3–5 years of experience in data engineering, cloud engineering, or analytics platform development.
- Minimum 2 years of hands-on experience developing solutions with Azure Databricks.
- Experience supporting enterprise data solutions in a manufacturing or industrial environment.
- Strong knowledge of Azure Databricks, Apache Spark, PySpark, Python, SQL, Delta Lake, lakehouse architecture, data modeling, and data warehousing concepts.
- Experience with Azure Data Lake Storage Gen2, Azure Data Factory, Azure DevOps, Git source control, and CI/CD practices for data platforms.
- Experience with data quality, security, monitoring, troubleshooting, and performance tuning.
- Knowledge of manufacturing data domains such as ERP, MES, production planning, inventory, supply chain, quality management, maintenance, or Industrial IoT.
- Ability to work with operational stakeholders and translate manufacturing processes into reliable data requirements and solutions.
- Experience with Microsoft Fabric, Power BI, or real-time and streaming data workloads.
- Hands-on experience with Unity Catalog and governed lakehouse implementations.
- Exposure to machine learning or AI data preparation and deployment patterns.
- Experience with Infrastructure as Code using Terraform, Bicep, or ARM templates.
- Preferred knowledge of Infor M3, including its application design, business data model, and core table structures used across manufacturing, supply chain, inventory, finance, and related functional areas.
- Preferred understanding of production, planning, inventory, quality, maintenance, and supply chain processes.
- Microsoft Certified: Azure Data Engineer Associate.
- Databricks Certified Data Engineer Associate or Professional.
- Microsoft Certified: Fabric Analytics Engineer Associate.
We are a family-owned business established in 1946 with nearly 5,000 employees. Over the years, Rosen’s Diversified, Inc. (“RDI”) has grown into a holding company of vertically integrated business units, including American Foods Group, America’s Service Line, Scientific Life Solutions, and Rosen’s Inc. By understanding our employees, our customers and ourselves, we are preparing RDI for the future generations of success.
Our Company is comprised of innovative entrepreneurs who value our casual and down to earth culture. As a member of the Rosen’s family, you will find yourself challenged and rewarded for your professional contributions as well as the Company’s success.
WHAT WE OFFER
- Privately held, family-owned (three generations) business which operates with a mentality of what should be done versus share holder requirements.
- Excellent health and welfare benefits including but not limited to medical, dental, vision, disability, and a variety of voluntary benefit options.
- 401(k) benefits with annual company match for eligible employees.
- Professional and personal development programs including Career and Learning Paths providing opportunities for advancement.
The Senior Cloud Data Engineer designs, develops, and optimizes scalable data solutions that support manufacturing operations, business intelligence, advanced analytics, and digital transformation. This role builds and maintains cloud-based data pipelines using Azure Databricks and related Azure services to integrate enterprise operational and business data. The ideal candidate combines hands-on cloud data engineering experience with collaboration across business stakeholders, analysts, architects, and data engineers to deliver reliable, secure, and high-performing data products that enable data-driven decisions across the organization.
Essential Functions and Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines using Azure Databricks, PySpark, Python, and SQL.
- Build and optimize ingestion frameworks for ERP, MES, Industrial IoT, quality, supply chain, inventory, finance, and related operational systems.
- Develop reusable engineering patterns and standards that improve delivery speed, supportability, and reliability.
- Implement and support lakehouse solutions using Delta Lake and Medallion Architecture across Bronze, Silver, and Gold layers.
- Design and optimize data models for reporting, analytics, AI, and machine learning use cases.
- Maintain data lineage, metadata, technical documentation, and operational runbooks.
- Integrate Azure Databricks with Azure Data Factory, Azure Data Lake Storage Gen2, Azure Key Vault, Microsoft Fabric, Power BI, and Azure DevOps.
- Support orchestration and movement of data between enterprise applications and cloud platforms.
- Monitor and optimize Databricks clusters, jobs, notebooks, and workflows for performance, scalability, and cost efficiency.
- Troubleshoot production issues, identify root causes, and implement sustainable corrective actions.
- Contribute to monitoring, alerting, testing, and operational support practices.
- Apply organizational standards for data quality, governance, privacy, security, and compliance.
- Implement role-based access controls and governed data access using Unity Catalog and Azure security services.
- Build automated validation, auditing, and data quality controls into engineering workflows.
- Partner with business users, analysts, architects, and IT teams to translate requirements into maintainable technical solutions.
- Participate in Agile ceremonies, sprint planning, code reviews, and technical design reviews.
- Mentor junior engineers and promote coding, testing, documentation, and deployment best practices.
- Bachelor's degree in computer science, information systems, data engineering, or a related field; an equivalent combination of education and experience may be considered.
- 3–5 years of experience in data engineering, cloud engineering, or analytics platform development.
- Minimum 2 years of hands-on experience developing solutions with Azure Databricks.
- Experience supporting enterprise data solutions in a manufacturing or industrial environment.
- Strong knowledge of Azure Databricks, Apache Spark, PySpark, Python, SQL, Delta Lake, lakehouse architecture, data modeling, and data warehousing concepts.
- Experience with Azure Data Lake Storage Gen2, Azure Data Factory, Azure DevOps, Git source control, and CI/CD practices for data platforms.
- Experience with data quality, security, monitoring, troubleshooting, and performance tuning.
- Knowledge of manufacturing data domains such as ERP, MES, production planning, inventory, supply chain, quality management, maintenance, or Industrial IoT.
- Ability to work with operational stakeholders and translate manufacturing processes into reliable data requirements and solutions.
- Experience with Microsoft Fabric, Power BI, or real-time and streaming data workloads.
- Hands-on experience with Unity Catalog and governed lakehouse implementations.
- Exposure to machine learning or AI data preparation and deployment patterns.
- Experience with Infrastructure as Code using Terraform, Bicep, or ARM templates.
- Preferred knowledge of Infor M3, including its application design, business data model, and core table structures used across manufacturing, supply chain, inventory, finance, and related functional areas.
- Preferred understanding of production, planning, inventory, quality, maintenance, and supply chain processes.
- Microsoft Certified: Azure Data Engineer Associate.
- Databricks Certified Data Engineer Associate or Professional.
- Microsoft Certified: Fabric Analytics Engineer Associate.
We are a family-owned business established in 1946 with nearly 5,000 employees. Over the years, Rosen’s Diversified, Inc. (“RDI”) has grown into a holding company of vertically integrated business units, including American Foods Group, America’s Service Line, Scientific Life Solutions, and Rosen’s Inc. By understanding our employees, our customers and ourselves, we are preparing RDI for the future generations of success.
Our Company is comprised of innovative entrepreneurs who value our casual and down to earth culture. As a member of the Rosen’s family, you will find yourself challenged and rewarded for your professional contributions as well as the Company’s success.
WHAT WE OFFER
- Privately held, family-owned (three generations) business which operates with a mentality of what should be done versus share holder requirements.
- Excellent health and welfare benefits including but not limited to medical, dental, vision, disability, and a variety of voluntary benefit options.
- 401(k) benefits with annual company match for eligible employees.
- Professional and personal development programs including Career and Learning Paths providing opportunities for advancement.
Cloud Data Engineer Jobs by Experience Level
Top Cities Hiring Cloud Data Engineers
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Find Cloud Data Engineer JobsCloud Data Engineer Job Market
Who's Hiring
- Google19
- Alvarez & Marsal13
- Rippling7
- Accenture6
- Logic6
Top Industries Hiring
- Technology & Software
- Aerospace & Defense
- Investment & Asset Management
- Consulting & Professional Services
- Automotive
What Employers Look For
The qualifications that appear most often in cloud data engineer jobs.
- Proficiency in SQL and at least one Python or Scala data engineering framework
- Hands-on experience building and maintaining pipelines on AWS, GCP, or Azure
- Experience with workflow orchestration tools such as Apache Airflow or Prefect
- Familiarity with columnar or lakehouse storage formats like Parquet, Delta Lake, or Iceberg
- Bachelor's degree in computer science, engineering, or a related technical field
- Knowledge of streaming platforms such as Apache Kafka or cloud-native equivalents
Tips for Your Cloud Data Engineer Job Search
Tailor your resume to pipeline tools
List the specific orchestration and transformation tools you've used, such as Apache Airflow, dbt, or Spark, with the cloud platform each ran on. Recruiters scan for exact tool names, and a generic 'data engineering' resume gets filtered out before a human reads it.
Quantify data volume and latency wins
Replace vague bullets like 'built data pipelines' with the scale you handled, the processing window you hit, or the cost you cut. Cloud data engineer hiring managers compare candidates on measurable outcomes, not responsibilities.
Filter openings by your cloud platform stack
Most cloud data engineer roles are anchored to one provider, AWS, GCP, or Azure, and the tooling differs significantly across them. Targeting listings that match your certified platform saves you from interviews where your hands-on experience doesn't map to the team's stack.
Apply early to roles that fit
Migrate Mate lists cloud data engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a system design answer for the data layer
Cloud data engineer interviews almost always include a whiteboard or verbal exercise asking you to design an ingestion and transformation pipeline from scratch. Practice explaining schema evolution, partitioning strategy, and idempotency out loud before your first screen.
Negotiate on compute credits and cert reimbursement
Beyond base compensation, ask whether the team provides cloud spend allowances for personal development or covers certification exams. These benefits are common in cloud-native orgs and often negotiable at offer time, especially for senior-level roles.
Cloud Data Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most cloud data engineers?
The companies hiring the most cloud data engineers right now include Google, Alvarez & Marsal, and Rippling, with the largest share of openings in California, Texas, and Virginia, based on current listings on Migrate Mate as of November 2026. Demand is especially concentrated in technology, financial services, and healthcare organizations running large-scale cloud data platforms.
How many cloud data engineer jobs are remote?
About 82% of cloud data engineer openings are fully remote or hybrid as of November 2026, making it one of the more location-flexible engineering disciplines. Roles focused on pipeline development, dbt modeling, and cloud infrastructure tend to be the most remote-friendly, while positions with heavy stakeholder collaboration or on-prem migration work are more likely to require on-site presence.
How do you become a cloud data engineer?
Start by building strong SQL fundamentals and learning Python for data transformation. Next, get hands-on with a major cloud platform by completing a certification like AWS Data Analytics or Google Professional Data Engineer. Build portfolio projects that demonstrate end-to-end pipelines using orchestration tools like Airflow and storage formats like Delta Lake, then target roles that match your certified platform.
Can you get a cloud data engineer job with little experience?
Yes, entry-level cloud data engineer roles exist, and employers hiring at that level focus on demonstrated technical ability over years of experience. Building two or three public pipeline projects on GitHub that show ingestion, transformation, and loading on a real cloud platform carries more weight than certifications alone. Targeting companies that run structured data engineering rotations or apprenticeships also improves your odds at the junior level.
What does the cloud data engineer interview process look like?
Most cloud data engineer interviews run in three to four stages. A recruiter screen covers your background and tool experience, followed by a technical phone screen with SQL or Python coding questions. The core round typically includes a pipeline design or system architecture exercise and a coding challenge focused on data transformation logic. A final round with the hiring manager or a panel covers past project depth and team fit.
Where can I find and apply to cloud data engineer jobs?
You can find and apply to cloud data engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your stack and experience level, then apply directly to each one that fits.
See All 178+ Cloud Data Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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