AI BI Data Engineer (Offshore)

Position SummaryMilacron 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 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 ResponsibilitiesData Engineering & Platform DevelopmentDesign develop and maintain endtoend data pipelines using Azure Data Factory Azure Databricks and SQLImplement and support Bronze Silver and Gold transformations using lakehouse and medallion architecture patternsIngest transform and integrate data from ERP CRM and operational systemsOptimize data pipelines for performance reliability and costMonitor troubleshoot and support production data workflowsAnalytics Data Modeling & Semantic EnablementDesign and implement analytical data models (fact/dimension star schema) with clearly defined grain keys and relationshipsCreate curated datasets and semantic-ready data domains that standardize KPI logic and business rulesEnsure consistent metric definitions and data logic across sources to support enterprise reporting and analyticsPartner with stakeholders to validate requirements definitions and acceptance criteria for analytics deliverablesPower BI Development & SupportDevelop and support Power BI semantic models datasets and reports aligned to enterprise data standardsTroubleshoot and optimize refresh reliability model performance and data-related reporting issuesProvide end-user and stakeholder support including issue triage root-cause analysis and clear documentationAI-Assisted & Agent-Enabled Data EngineeringEnable AI-assisted analytics and agent-driven use cases by delivering well-modeled well-documented datasets and clear schemasUse LLM-powered tools to accelerate code scaffolding documentation and pattern development; ensure all outputs are reviewed tested and production-readySupport lightweight automation/agent workflows for well-defined tasks within approved guardrails and controlsData Quality Governance & OperationsImplement data quality rules validation checks and reconciliation tests to ensure dataset accuracy and completenessApply logging monitoring and basic observability so pipelines and automated workflows are reliable and auditableFollow Azure security and access-control practices including role-based access control (RBAC)Document pipelines transformations models and KPI logic for traceability lineage and supportParticipate in CI/CD and release processes using Azure DevOpsCollaboration & TeamingWork with a variety of teams including data system and business teams to understand requirements and deliver reliable data solutionsCollaborate with onshore and offshore teammates following established standards and designs and communicate clearly to ensure solutions are understandable repeatable and maintainableRequired QualificationsBachelors degree in Computer Science Data Engineering Information Systems or related field3 years of experience in data engineering analytics engineering or BI engineeringStrong 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 layersHands-on experience with:Azure DatabricksAzure Data Factory or similar orchestration toolsLakehouse/medallion architecture patternsExperience working with Apache Spark (Spark SQL and/or PySpark) in a Databricks environmentExperience developing or supporting Power BI datasets semantic models or reportsFamiliarity with CI/CD version control and production support practicesStrong problem-solving communication and documentation skillsPreferred QualificationsWorking knowledge of Python for data engineering automation or notebookbased workflowsExperience working in a global or offshore delivery modelKnowledge of Delta Lake data validation or data observability conceptsExposure to AIassisted analytics conversational BI or agentbased workflowsERP or CRM data experience (e.g. JD Edwards Salesforce)Azure or Databricks certifications (e.g. DP203 or equivalent)Experience building lightweight data applications or dashboards (e.g. Power BI Databricks SQL dashboards notebooks or lowcode apps) to support analytics or business workflows is a plusWhat Success Looks LikeProduction data pipelines are reliable monitored and well documentedAnalytics data models and semantic domains consistently support enterprise KPIs and reportingPower BI datasets and reports refresh reliably and perform well for end usersData quality and validation reduce defects and improve trust in reportingStakeholders receive timely support clear communication and actionable documentationData products are ready to support AI-assisted analytics and controlled agent-enabled workflows