Job Title: Data Architect
Location: Washington, DC
Department: IT/Technology
Reports To: IT Manager/IT Director
Compensation: Up to $80/Hour & 170,000/Year
Qualifications and Experience:- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
- At least 10 years of hands-on experience in data warehousing, data mining, and data modeling.
- Strong ability to identify and define strategic data requirements for large-scale organizations.
- Expertise in determining data storage, utilization strategies, and ensuring data integrity across an enterprise.
- Proven track record in designing, developing, and maintaining sophisticated data architectures.
- In-depth understanding of data mining techniques, statistical analysis, and advanced analytical methods.
- Significant experience working with large and complex datasets, including data preparation for predictive and prescriptive modeling.
- Advanced proficiency in SQL, ETL processes, data modeling, and database management.
- Expert-level knowledge of data mining tools and platforms such as Python, R, SAS, or other similar technologies.
- Hands-on experience with big data technologies (e.g., Hadoop, Spark, Databricks) and cloud platforms (e.g., AWS, Azure, Google BigQuery, Snowflake).
- Strong skills in selecting and managing the appropriate database management technologies, tools, and interfaces to meet business needs.
- Experience optimizing data systems and infrastructure to ensure scalability, performance, and data integrity.
- Ability to advise development teams, data analysts, and business stakeholders on best practices for data management, standards, and governance.
- Excellent communication skills, with the ability to translate complex technical information into clear terms for non-technical audiences.
- Experience in working within cross-functional teams and collaborating with business stakeholders to meet data and analytics requirements.
Responsibilities: Data Architecture Design & Optimization:- Design, develop, and maintain scalable and high-performing data architectures, ensuring availability, reliability, and performance of all data systems.
- Continuously optimize and enhance data systems to align with the enterprise's evolving needs.
Data Modeling & Mining:- Develop robust data models to transform raw data into valuable, actionable insights.
- Ensure that data models and architectures are capable of managing large and diverse datasets for advanced analytics, including predictive and prescriptive modeling.
Data Preparation for Advanced Analytics:- Prepare and clean data for advanced analytical processes, utilizing tools such as Python, R, SQL, and others to ensure data readiness for complex modeling.
- Implement data wrangling and transformation techniques to enhance data quality and usability for modeling purposes.
Testing & Quality Assurance:- Develop and execute comprehensive testing plans for data processes, ensuring accuracy, consistency, and integrity in all datasets.
- Automate data pipelines to ensure scalability, validate data quality, and maintain high standards of data governance.
Collaboration & Cross-Functional Support:- Collaborate closely with data engineers, data scientists, business analysts, and other stakeholders to ensure that data infrastructure aligns with strategic organizational goals.
- Serve as the subject matter expert, providing guidance and support to teams in effectively leveraging data architectures and tools.
Tool Utilization & Automation:- Leverage advanced tools and technologies to streamline ETL processes and data workflows, ensuring efficiency and minimizing manual intervention.
- Implement automation strategies to optimize data preparation and processing workflows, enhancing overall productivity.
Documentation & Knowledge Sharing:- Maintain clear and comprehensive documentation for data management practices, models, and processes to ensure consistency, reproducibility, and knowledge sharing.
- Lead training sessions and workshops to upskill internal teams on best practices in data architecture, management, and analytics.
Data Governance & Compliance:- Ensure adherence to data governance frameworks, enforcing data privacy, security, and compliance with relevant regulatory standards.
- Implement and enforce best practices for data handling, storage, and processing across the organization.