Architect - Data Engineering
2 hours ago
What You Will Do:
- Design and lead end-to-end architecture for modern, cloud-native data engineering, AI/ML and analytics platforms across the full data lifecycle, including ingestion, storage, transformation, and consumption.
- Architect high-performance data solutions using Azure Databricks, Fabric, Snowflake, AWS Redshift, Power BI, Tableau, Python, and other relevant technologies.
- Collaborate with technology leadership and engineering teams to align solutions with enterprise strategy, business goals, and innovation roadmaps.
- Define and enforce standards for data quality, metadata, lineage, and governance in partnership other teams using market leading tools.
- Provide architectural guidance for AI/ML integrations, including data preparation, feature engineering, and model deployment support.
- Conduct design reviews, architectural assessments, and performance tuning to ensure system reliability, scalability, and maintainability.
- Develop and maintain reusable patterns, frameworks, and coding standards in ADF, Python, PySpark, and SQL.
- Collaborate with product managers, engineering leads, analysts, and data scientists to deliver high-impact, cross-functional solutions.
- Drive the evaluation and adoption of emerging technologies in cloud data platforms, streaming analytics, and intelligent automation.
- Mentor data engineers and oversee best practices in solution design, code quality, documentation, and continuous improvement.
- Support DevOps and DataOps initiatives by integrating CI/CD pipelines, Git workflows, and automated testing into engineering workflows.
What You Will Need:
- Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or a related field.
- 10-12 years of progressive experience in data engineering, data modelling including at least 5 years in an architecture-focused or lead technical role.
- Proven experience architecting enterprise-grade cloud data platforms using Azure (and AWS).
- Deep expertise in technologies such as Azure Data Factory, Snowflake, Databricks, Spark/PySpark, and SQL-based processing.
- Strong grasp of modern data architecture paradigms including data lakehouse, data fabric, microservices, and event-driven design patterns.
- Solid understanding of DevOps and Infrastructure-as-Code practices, including CI/CD pipelines, Docker/Kubernetes, and automated deployment frameworks.
What Would Be Nice To Have:
- Familiarity with modern data architecture frameworks, including data mesh, data fabric, and data lakehouse.
- Industry experience in Healthcare, Finance or other highly data-driven domains.
- Experience with Database Lifecycle Management (DLM) tools and strong understanding of CI/CD pipelines, branching strategies, and collaborative DevOps practices.
- Hands-on experience integrating with data governance and master data management platforms s.
- Proficiency in Python, Scala, or similar programming languages commonly used in large-scale data engineering.
- Understanding of AI/ML model lifecycle architecture, including data preparation, model training, and production deployment best practices.
- Relevant certifications in cloud architecture (e.g., Azure Solutions Architect, AWS Certified Data Analytics) or enterprise architecture (e.g., TOGAF Framework
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