M Senior Azure Cloud Platform Engineer MCA Connect Remote

Through passion and deep industry expertise, MCA Connect helps manufacturers succeed by unlocking innovation with actionable business insights. Our strategic solutions, innovation, and industry intelligence help manufacturers gain visibility, improve profitability, and achieve a competitive edge. Established in 2002, MCA Connect has grown into one of the largest US-based solution partners in Microsoft Business Applications and Azure Data & AI / Digital & App Innovation.

Our Microsoft Specialties include Finance and Supply Chain, Analytics on Azure, Data Warehouse Migration, and Power Platform. We’re also a fifteen-time Microsoft Partner of the Year and three-time Inc. Best Workplaces award winner.

Senior Azure Cloud Platform/Data Science Engineer

Location:

Remote with light travel as needed to client sites

Employment Type:

Full-time

We are seeking a Senior Data Science Engineer with strong Python and SQL skills, quantitative judgment, and demonstrated strength in Azure. The role combines data science, data engineering, and software development. Practical working proficiency in Power BI, Microsoft Fabric, and Azure Databricks is required, alongside effective use of AI development tools and independent validation of results.

• At least 5 years of relevant professional experience in data science, data engineering, or software engineering, including at least 3 years building, deploying, and supporting workloads in Microsoft Azure. Candidates should demonstrate ownership of deployed solutions and the ability to troubleshoot operational issues.
• A bachelor's degree in operations research, business, computer science, data science, information systems, statistics, mathematics, engineering, or a related field, or equivalent demonstrated professional experience.
• A master's degree is a plus but is not required.
• A PhD is not required.

• Microsoft Azure:
  Strong hands-on capability in data storage and compute, identity and access, secure connectivity, deployment automation, monitoring, troubleshooting, and cost management. Relevant services include ADLS Gen2, Azure SQL, Entra , managed identities, Key Vault, and Azure Monitor.
• Power BI:
  Working proficiency building reports and semantic models, using Power Query and DAX, defining relationships and measures, configuring refresh and row-level security, and reconciling reported metrics to source data.
• Microsoft Fabric:
  Practical experience with work spaces, One Lake, lake houses or warehouses, and data pipelines or notebooks. Ability to follow data lineage, manage appropriate access, and diagnose processing or reporting issues.
• Azure Databricks:
  Working proficiency with notebooks, PySpark or Spark SQL, Delta Lake tables, and scheduled jobs. Ability to investigate failed runs and understand compute configuration, data permissions, and performance trade-offs.
• Python and SQL:
  Strong proficiency in data preparation, analysis, reusable components, and backend processing. Ability to read unfamiliar code, explain its behavior, and improve its maintainability.
• Data modeling and integration:
  Understand analytical data models, schemas, data grain, join cardinality, missing values, time alignment, and traceability across data sources and reports.
• Software delivery:
  Apply version control, code review, automated testing, and controlled releases. Understand APIs, persistent state, long-running processing, and safe recovery from failures.

Demonstrate agentic development capability with tools such as Claude Code and OpenAI Codex. Break work into bounded tasks, provide repository context and constraints, direct coding agents, and iterate through implementation, testing, and debugging. Review generated changes, manage branches and tool permissions, protect sensitive information, and independently verify code and analytical results before release.

Translate ambiguous business questions into measurable analytical problems, with explicit assumptions, objectives, constraints, and success criteria. Apply statistical and mathematical reasoning, including weighted metrics, uncertainty, validation design, data leakage, and the distinction between association and causation. Assess data completeness, consistency, and suitability before choosing analytical methods. Reconcile inputs, intermediate calculations, and reported outputs. Evaluate models and analytical results against meaningful baselines.

Distinguish…