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Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-10-21T17:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-21T17:35:00Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Responsibilities: \nDefine and own the enterprise data and AI architecture vision, reference architecture, and design standards.\nArchitect end-to-end data platforms on Azure and Databricks using modern lakehouse and cloud-native principles.\nDesign scalable frameworks for batch, streaming, real-time, and incremental data processing.\nEstablish architecture patterns for ingestion, transformation, storage, serving, and consumption of data across domains.\nLead the design of AI-ready data foundations, including curated datasets, feature pipelines, semantic layers, and model-serving data interfaces.\nEnsure data architecture supports advanced analytics, machine learning, GenAI, and intelligent automation use cases.\nPartner with data engineers, data scientists, platform teams, and business stakeholders to translate business objectives into robust technical solutions.\nProvide technical leadership on Spark optimization, Databricks workload tuning, Delta Lake design, and performance engineering.\nDefine and enforce standards for data governance, metadata, lineage, privacy, security, and compliance.\nDrive adoption of data quality, observability, and reliability practices across the platform.\nGuide implementation of orchestration, CI/CD, testing, and release management for data products and pipelines.\nEvaluate emerging technologies and recommend architectural patterns that improve scalability, resilience, and cost efficiency.\nMentor engineering teams and act as a design authority for critical data and AI initiatives.\n\nRequirements: \nBachelor's or master's degree in computer science, information technology, engineering, or a related field.\n10+ years of experience in data engineering, data architecture, data platform engineering, or related disciplines.\nStrong hands-on experience with Azure, Azure Databricks, Apache Spark, PySpark, SQL, and Delta Lake.\nProven experience designing enterprise-grade data architectures and modern lakehouse platforms.\nStrong expertise in complex data processing, including batch, streaming, and near real-time pipelines.\nSolid understanding of data modeling, domain-oriented data design, and reusable data products.\nWorking knowledge of AI/ML data pipelines, feature engineering, model data preparation, and data enablement for AI solutions.\nExperience with data governance, security, access control, privacy, lineage, and quality management.\nExposure to orchestration tools such as Azure Data Factory, Databricks Workflows, Airflow, or equivalent platforms.\nStrong ability to balance strategic architecture thinking with practical implementation guidance.\nExcellent communication, stakeholder management, and problem-solving skills.\n\nCompetencies: \nStrong architectural thinking and solution ownership.\nAbility to lead complex, cross-functional technology initiatives.\nStrong analytical and problem-solving mindset.\nExcellent collaboration and influencing skills.\nAbility to work in a fast-paced, evolving technology environment.\nHigh attention to detail and commitment to quality.\n\nRole Expectations: \nEstablish a future-ready data and AI architecture that enables business growth and innovation.\nCreate scalable, governed, and reusable data platform capabilities.\nImprove data engineering maturity across ingestion, processing, governance, and consumption layers.\nEnable AI and advanced analytics through reliable, high-quality, and well-architected data foundations.\nServe as a trusted advisor to technology and business leadership on data and AI 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