Software Engineer, Advanced Analytics (Onsite)

You will bring together software engineering, analytics, and robust deployment practices to move analytics solutions reliably from prototype to production. This includes building scalable pipelines, deploying and monitoring analytics applications, integrating enterprise systems and services, and ensuring security, governance, and operational reliability. You have experience building and deploying production analytics solutions and enjoy solving challenges at the intersection of software engineering, industrial data, and cloud platforms. We value strong engineering fundamentals, hands-on deployment experience, and the ability to collaborate with data scientists, domain experts, and business stakeholders., * Design, build, and maintain end-to-end analytics pipelines from development through deployment and monitoring.

• Write production-quality Python code using modern software engineering practices, including testing, logging, and error handling.
• Design deployment architectures that can run across Databricks, Azure, containers, and other cloud environments.
• Implement orchestration patterns including routing, handoffs, fallback logic, and failure handling.
• Develop resilient analytics capable of handling real-world data challenges such as missing data, delayed data, communication outages, sensor failures, and inconsistent telemetry.
• Build and support analytics solutions using time-series data from OT, IoT, and industrial systems.
• Develop CI/CD pipelines using GitHub Actions, Azure DevOps, or similar technologies.
• Establish monitoring, observability, and operational support for analytics applications, services, and pipelines.
• Perform data analysis using SQL to support AI and pipeline development.
• Develop operational dashboards and visualizations to monitor analytics performance, system health, data quality, and business outcomes.
• Collaborate with engineering, analytics, product, and business teams to deliver scalable solutions.
• Apply security, governance, and Responsible AI practices including access controls, auditability, and data protection.

• Background in Software Engineering, Analytics Engineering, Data Engineering, AI Engineering, or a related technical field.
• Strong Python skills including design patterns, testing, packaging, logging, and API integration.
• Hands-on experience building, deploying, and supporting production software or analytics solutions.
• Experience designing and maintaining deployment pipelines, CI/CD workflows, monitoring, and operational support for production systems.
• Experience working with time-series data and industrial, IoT, or OT data sources.
• Understanding of common operational data challenges including missing, delayed, noisy, or unreliable sensor data.
• Knowledge of electrical power systems, energy systems, industrial automation, or related engineering domains.
• Experience deploying applications using containerized and cloud-native architectures.
• Experience designing solutions across Databricks, Azure, and similar environments.
• Experience with GitHub Actions, Azure DevOps, or equivalent CI/CD platforms.
• Strong SQL (Spark SQL / Delta; relational databases a plus)
• Familiarity with Power BI, Tableau, or Looker.
• Bachelor’s degree with 5+ years of relevant experience, or Master’s degree with 3+ years of relevant experience.

Other Skills:

• Experience with Databricks Workflows, Delta Live Tables, or Apache Spark.
• Experience with Azure Machine Learning, Azure AI Foundry, AKS, or Container Apps.