Job Description
Job Summary:
We are seeking an experienced and forward-thinking Data Architect to lead the design, development, and implementation of advanced data warehousing and analytics solutions on Microsoft Azure. This role blends architecture, engineering, analytics, AI, and leadership to deliver data-driven capabilities that empower strategic business decisions.
Key Responsibilities
Data Warehouse Architecture & Development
- Lead the design and implementation of scalable, high-performance data warehousing solutions using Microsoft Azure technologies (e.g., Azure Synapse Analytics, Azure Data Lake, SQL Server).
- Build and manage robust ETL/ELT pipelines for efficient data ingestion and integration from diverse sources.
- Define and execute strategies for data storage, retrieval, and long-term archiving of large-scale datasets.
Analytics & Business Intelligence
- Collaborate with stakeholders to gather analytics and reporting requirements and develop solutions that drive data-informed decisions.
- Design and build dynamic dashboards and reports using Power BI and other BI tools.
- Conduct advanced data analysis to deliver actionable insights that support strategic planning.
AI & Predictive Modeling
- Design, develop, and operationalize machine learning and AI models to support predictive analytics and business forecasting.
- Utilize Microsoft Azure AI and ML services (e.g., Azure Machine Learning, Databricks) for model development and deployment.
- Partner with business leaders to identify opportunities for AI-driven solutions.
Data Science Enablement
- Serve as a bridge between business units and the data science team to align solutions with business goals.
- Support the creation and maintenance of data pipelines used for training and scoring machine learning models.
- Implement automation and optimization strategies across data science workflows.
Project Leadership & Team Development
- Lead end-to-end data infrastructure projects, from initial planning to production rollout.
- Mentor junior data professionals, fostering knowledge-sharing and adherence to best practices in data engineering and analytics.
- Manage legacy data migrations and integrations into modern Azure-based architectures.
Innovation & Continuous Improvement
- Stay current with industry trends and emerging technologies in data engineering, AI, and analytics.
- Advocate for the adoption of modern tools and techniques to enhance data capabilities and analytical outcomes.
- Continuously assess and improve infrastructure scalability, performance, and reliability to support evolving business needs.
Qualifications
Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
- 8+ years of experience in data architecture, data warehousing, and business analytics within enterprise environments.
- Strong experience with Microsoft Azure data services (Azure Synapse, Azure SQL, Data Lake, etc.).
- Hands-on experience in building and deploying AI/machine learning models, including predictive analytics and statistical modeling.
Technical Skills
- Expertise in SQL and data modeling for structured and semi-structured data.
- Proficient with data pipeline tools such as Azure Data Factory and SSIS.
- Skilled in BI platforms like Power BI, Tableau, or similar.
- Knowledge of data visualization principles and best practices.
- Strong foundation in machine learning techniques, statistical modeling, and data science tools (e.g., Python, R, Azure ML, Databricks).
- Familiarity with DevOps, source control (Git), and CI/CD pipelines in data environments.
Soft Skills
- Strong verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders.
- Excellent analytical and problem-solving capabilities.
- Proven ability to work both independently and in collaborative, cross-functional teams.
- Leadership experience, including mentoring junior engineers and data scientists.
Preferred Qualifications
- Microsoft Azure certifications (e.g., Azure Data Engineer Associate, Azure AI Engineer Associate).
- Experience with advanced analytics methods such as deep learning, NLP, or time-series forecasting.
- Knowledge of big data tools (e.g., Hadoop, Spark) and their integration with Azure platforms.
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