Senior Azure Cloud Platform Engineer

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 ID, 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 workspaces, OneLake, lakehouses 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 tradeoffs.
• 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 improved model performance from changes in data selection, aggregation, or assumptions.
• Reproduce unexpected behavior, construct small diagnostic examples, and use evidence to distinguish data, modeling, and implementation problems.
• Design reproducible experiments and regression and integration tests. Verify behavior across processing, storage, APIs, and reporting boundaries.
• Diagnose performance and usability issues involving large datasets, long-running tasks, stale results, and failure reporting.
• Explain methods, tradeoffs, uncertainty, and limitations clearly to technical and nontechnical stakeholders.

Manufacturing experience and/or experience in the Microsoft partner channel is especially valuable. The following qualifications strengthen a candidate profile; depth in every area is not expected.

• Manufacturing knowledge, such as production planning, bills of material, material requirements planning, scheduling, procurement, or inventory processes.
• Microsoft partner-channel experience in technical consulting, solution architecture, implementation, or integrations, including exposure to Dynamics 365 or other ERP systems.
• Predictive modeling, time-series forecasting, simulation, mathematical optimization, or model deployment and monitoring.
• Infrastructure as code with Bicep or Terraform, CI/CD with Azure DevOps or GitHub Actions, and automated environment provisioning.
• FastAPI, React, JavaScript, data visualization, and interactive analytical applications.
• Relevant Microsoft or Databricks certifications. Certifications support, but do not replace, demonstrated practical capability.

Learn unfamiliar business concepts through collaboration with subject-matter experts. Restate proposed methods accurately, identify their assumptions, test their implementation, and question implausible results. Advanced operations research expertise is welcome; strong quantitative foundations and demonstrated learning ability are essential.

• Take ownership of quality, follow issues through to a verified resolution, and communicate progress and blockers clearly.
• Distinguish what the evidence establishes from what remains an assumption, and document decisions so others can reproduce the work.
• Balance analytical rigor with practical delivery, seek feedback early, and recognize when deeper domain or platform expertise is needed.