AI / BI Data Engineer (Offshore)

Position Summary Milacron is seeking an AI/BI Data Engineer to build and support production-grade data pipelines and analytics data products that power enterprise reporting, KPI measurement, and AI-enabled insights. The ideal candidate is hands-on in Databricks/Spark and SQL, comfortable working across ingestion, transformation, and modeling, and able to partner with business and system teams to translate requirements into reliable datasets and Power BI-ready semantic models. Key Responsibilities Data Engineering & Platform Development Design, develop, and maintain end‑to‑end data pipelines using Azure Data Factory, Azure Databricks, and SQL Implement and support Bronze, Silver, and Gold transformations using lakehouse and medallion architecture patterns Ingest, transform, and integrate data from ERP, CRM, and operational systems Optimize data pipelines for performance, reliability, and cost Monitor, troubleshoot, and support production data workflows Analytics, Data Modeling & Semantic Enablement Design and implement analytical data models (fact/dimension, star schema) with clearly defined grain, keys, and relationships Create curated datasets and semantic-ready data domains that standardize KPI logic and business rules Ensure consistent metric definitions and data logic across sources to support enterprise reporting and analytics Partner with stakeholders to validate requirements, definitions, and acceptance criteria for analytics deliverables Power BI Development & Support Develop and support Power BI semantic models, datasets, and reports aligned to enterprise data standards Troubleshoot and optimize refresh reliability, model performance, and data-related reporting issues Provide end-user and stakeholder support, including issue triage, root-cause analysis, and clear documentation AI-Assisted & Agent-Enabled Data Engineering Enable AI-assisted analytics and agent-driven use cases by delivering well-modeled, well-documented datasets and clear schemas Use LLM-powered tools to accelerate code scaffolding, documentation, and pattern development; ensure all outputs are reviewed, tested, and production-ready Support lightweight automation/agent workflows for well-defined tasks within approved guardrails and controls Data Quality, Governance & Operations Implement data quality rules, validation checks, and reconciliation tests to ensure dataset accuracy and completeness Apply logging, monitoring, and basic observability so pipelines and automated workflows are reliable and auditable Follow Azure security and access-control practices, including role-based access control (RBAC) Document pipelines, transformations, models, and KPI logic for traceability, lineage, and support Participate in CI/CD and release processes using Azure DevOps Collaboration & Teaming Work with a variety of teams, including data, system, and business teams, to understand requirements and deliver reliable data solutions Collaborate with onshore and offshore teammates, following established standards and designs, and communicate clearly to ensure solutions are understandable, repeatable, and maintainable Required Qualifications Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or related field 3+ years of experience in data engineering, analytics engineering, or BI engineering Strong SQL skills (complex joins, transformations, performance tuning) Experience designing analytical data models (e.g., fact and dimension tables) that support KPI calculation, reporting, and semantic layers Hands-on experience with: Azure Databricks Azure Data Factory or similar orchestration tools Lakehouse/medallion architecture patterns Experience working with Apache Spark (Spark SQL and/or PySpark) in a Databricks environment Experience developing or supporting Power BI datasets, semantic models, or reports Familiarity with CI/CD, version control, and production support practices Strong problem-solving, communication, and documentation skills Preferred Qualifications Working knowledge of Python for data engineering, automation, or notebook‑based workflows Experience working in a global or offshore delivery model Knowledge of Delta Lake, data validation, or data observability concepts Exposure to AI‑assisted analytics, conversational BI, or agent‑based workflows ERP or CRM data experience (e.g., JD Edwards, Salesforce) Azure or Databricks certifications (e.g., DP‑203 or equivalent) Experience building lightweight data applications or dashboards (e.g., Power BI, Databricks SQL dashboards, notebooks, or low‑code apps) to support analytics or business workflows is a plus What Success Looks Like Production data pipelines are reliable, monitored, and well documented Analytics data models and semantic domains consistently support enterprise KPIs and reporting Power BI datasets and reports refresh reliably and perform well for end users Data quality and validation reduce defects and improve trust in reporting Stakeholders receive timely support, clear communication, and actionable documentation Data products are ready to support AI-assisted analytics and controlled agent-enabled workflows