Job Description:
Data Architect / Lead
Location: Dallas, TX(Hybrid)
Certifications in data management or cloud platforms (e.g., AWS Certified Big Data Specialty, Google Professional Data Engineer).
Experience with machine learning and AI-driven data architectures.
Knowledge of microservices and API-driven data integration.
Proficiency in programming languages such as Python, Java, or Scala.
The Digital Platform Data Architect is responsible for defining and managing the overall data architecture for digital platforms, ensuring that data is structured, stored, and accessed efficiently. This includes working with large datasets, integrating data from multiple sources, and implementing data governance policies. The Data Architect will collaborate with stakeholders across the organization to design data models, establish data standards, and support data-driven decision-making.
Key Responsibilities:
Data Architecture Design:
Design scalable and efficient data architectures that support the organization’s digital platforms and applications.
Develop logical and physical data models that represent business processes and data flows.
Define data storage solutions, including relational databases, data lakes, data warehouses, and NoSQL databases, based on use cases and requirements.
Data Integration and Management:
Lead the integration of data from various sources, ensuring consistency, accuracy, and timeliness.
Implement data pipelines and ETL (Extract, Transform, Load) processes to facilitate data movement and transformation.
Manage and optimize the performance of databases and data storage systems, ensuring low latency and high availability.
Data Governance and Security:
Establish data governance frameworks, including data quality standards, data lineage, and metadata management.
Define and enforce data security policies, including data encryption, access controls, and compliance with relevant regulations (e.g., GDPR, HIPAA).
Ensure data privacy and protection measures are in place, particularly for sensitive and personally identifiable information (PII).
Collaboration and Communication:
Work closely with business stakeholders, data scientists, data engineers, and software developers to understand data requirements and translate them into architectural solutions.
Provide guidance on data-related issues and best practices, ensuring alignment between data architecture and business objectives.
Document data architecture designs, data models, and processes for knowledge sharing and future reference.
Innovation and Continuous Improvement:
Stay informed about the latest data technologies, tools, and industry trends to drive innovation in data architecture.
Evaluate and recommend new data management technologies and platforms to improve the organization’s data capabilities.
Lead efforts to improve data architecture, including refactoring, optimization, and migration of legacy systems to modern platforms.
Data Analytics and Reporting:
Support the development of data analytics platforms by ensuring that data architectures are designed for efficient querying and reporting.
Collaborate with data analysts and business intelligence teams to develop dashboards, reports, and data visualizations.
Implement data cataloging and discovery tools to enable self-service data access for business users.
Required Qualifications:
Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field.
Extensive experience in data architecture, data modeling, and database design.
Proficiency with data management tools and technologies, including relational databases (e.g., SQL Server, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and cloud data platforms (e.g., AWS Redshift, Azure Synapse).
Strong understanding of data integration, ETL processes, and data warehousing concepts.
Knowledge of data governance practices, data security standards, and regulatory compliance requirements.
Experience with big data technologies, such as Hadoop, Spark, or Kafka.
Familiarity with data analytics tools, such as Tableau, Power BI, or Looker.
Preferred Qualifications:
Advanced degree (Master’s or PhD) in a relevant field.
Certifications in data management or cloud platforms (e.g., AWS Certified Big Data Specialty, Google Professional Data Engineer).
Experience with machine learning and AI-driven data architectures.
Knowledge of microservices and API-driven data integration.
Proficiency in programming languages such as Python, Java, or Scala.
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