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Senior Data Architect
1 month ago
Responsibilities:
- Design and implement end-to-end data architecture solutions on Azure/Palantir and future state cloud platforms.
- Collaborate with business stakeholders, data engineers, and data scientists to understand data requirements and design scalable and flexible data models.
- Develop data ingestion strategies and frameworks to efficiently capture and integrate data from various sources into Azure and other cloud data services.
- Define data storage and retrieval mechanisms, including multi-tenant data partitioning, indexing, and compression, to optimize performance and cost efficiency.
- Implement data governance and data management practices to ensure data quality, consistency, and compliance with relevant regulations.
- Create and maintain data pipelines and ETL/ELT processes to transform, cleanse, and integrate data across different data sources.
- Collaborate with security and compliance teams to establish and enforce data security measures, including data encryption, access controls, and data masking.
- Perform data profiling, analysis, and troubleshooting to identify and resolve data-related issues.
- Stay up to date with the latest advancements in Azure Cloud data services and recommend innovative solutions to enhance data architecture capabilities.
- Provide technical guidance and mentorship to junior team members and promote best practices in data architecture design and implementation.
Qualifications:
- Bachelor's or Master's degree in computer science, engineering, or a related field.
- Extensive experience designing and implementing data architecture solutions on Azure and other cloud platforms.
- Strong proficiency in Azure data services such as Azure SQL Database, Azure Synapse Analytics, Azure Data Lake Storage, Azure Databricks, and Azure Cosmos DB.
- Relevant certifications in Azure or data architecture preferred.
- Deep understanding of data modeling principles and experience with relational, dimensional, and NoSQL data modeling techniques.
- Solid knowledge of ETL/ELT processes, data integration patterns, and data warehouse concepts.
- Familiarity with big data processing frameworks like Apache Spark or Hadoop.
- Proficiency in SQL and programming languages such as Python or Scala.
- Pandas, Spark and PySpark experience.
- Ability to debug PySpark based pipelines.
- Distributed Data Pipeline engineering.
- Software development lifecycle knowledge and understanding.
- Experience programmatically processing data from a range of sources including spreadsheets, website APIs, databases and more.
- Understanding of data lake architectures.
- Understanding of data visualization and reporting tools preferred.
- Understanding of modern data storage formats (parquet etc.) and optimization strategies.
- Data security engineering and best practices.
- Experience in data validation and test writing for spark pipelines.
- Streaming data systems, real time data processing knowledge (Kafka, Azure Event Hub, AWS SQS etc.).
- Familiarity with DevOps practices and CI/CD pipelines in the context of data architecture preferred.
- Data archival and historical provenance and lineage experience.
- Requirements gathering and analysis experience.
- Experience with data governance, data security, and compliance practices in Azure and other cloud platforms.
- Knowledge of machine learning and AI technologies and their integration with data architecture preferred.
- Experience with banking, financial crimes, compliance, case management preferred.
- Strong problem-solving and analytical skills with the ability to troubleshoot and resolve complex data-related issues.
- Highly effective communication and documentation skills.
- Excellent communication and collaboration skills to work effectively with stakeholders at all levels.
- Ability to manage multiple projects and priorities in a fast-paced and dynamic environment.
- Ability to prioritize and manage deliverable timelines and deadlines.
- Excellent project management skills with the ability to execute, meet deadlines, and work with a sense of urgency to deliver high-quality and accurate results.
- Strong organization and prioritization skills, with the ability to multitask and manage competing deadlines, with excellent attention to detail.
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