Data Architect

As a Data Architect, you will play a pivotal role in owning the organization’s data architecture and will be responsible for designing, building, implementing, and managing this architecture across all applications within the enterprise.

The role demands a strong understanding of data modeling, database management, data warehousing, and data integration techniques, requiring impeccable communication skills and attention to detail.

Additionally, you will collaborate closely with stakeholders, including data engineers, analysts, and business leaders, to ensure that data architecture aligns with organizational data strategy and requirements.

ESSENTIAL ROLES AND RESPONSIBILITIES

• Design enterprise data management systems that will provide data insights to support the business in order to make key decisions.
• Identify key systems of record and modernize data flow to support it.
• Develop and implement the organization's data architecture strategy, including conceptual, logical, and physical data models, ensuring alignment with business goals and industry best practices.
• Oversee the selection, implementation, and optimization of all data systems, ensuring efficient data storage, retrieval, and integrity.
• Establish data governance policies, procedures, and standards to ensure data quality, cost, consistency, privacy, and security throughout the data lifecycle.
• Identify and address performance bottlenecks within the data architecture through optimization techniques, indexing strategies, and data partitioning.
• Collaborate with cross-functional teams, including data engineers, analysts, developers, and business stakeholders, to understand requirements, define data architecture solutions, and communicate technical concepts effectively.
• Stay abreast of emerging trends and technologies in data management, analytics, and cloud computing, evaluating their potential impact on the organization's data architecture and recommending adoption strategies accordingly.
• Maintain comprehensive documentation of data architecture artifacts, including data models, data dictionaries, metadata repositories, and technical specifications.
• Continuously assess and refine the data architecture framework, processes, and standards to enhance scalability, reliability, and performance in alignment with evolving business needs and technological advancements.
• Support and advocate for process improvement to result in higher quality data assets throughout the organization.

EDUCATION, SKILLS, AND EXPERIENCE

• Bachelor's or Master's degree in Computer Science, Information Systems, or related field.
• Technical data lead with 5+ years of experience in a similar role.
• Understands SOA and AWS Cloud.
• Understands Data Science, Reporting, and Orchestration concepts and can put into practice for a travel company.
• In-depth knowledge of data modeling principles, techniques, and modern tools (e.g., ER / Studio, PowerDesigner).
• Proficiency in database management systems, including relational databases (e.g., Oracle, SQL Server, MySQL) and NoSQL databases (e.

g., MongoDB, Cassandra).

• Hands-on experience with data integration tools and technologies (e.g., ETL / ELT, APIs, messaging queues, Informatica, Talend, AWS Glue, AWS Step Functions, Fivetran, and Apache Airflow).
• Strong understanding of data governance, security, and compliance requirements (e.g., GDPR, HIPAA, PCI DSS).
• Familiarity with cloud platforms (e.g., AWS, Azure) and their data services (e.g., S3, Redshift, BigQuery).
• Self-starter with excellent analytical, problem-solving, and decision-making skills with a keen attention to detail.
• Effective communication and interpersonal skills, with the ability to collaborate across diverse teams and stakeholders.
• Proven ability to manage multiple priorities and deliver results in a fast-paced, dynamic environment.

PREFERRED QUALIFICATIONS

• Certification in data management or related field (e.g., AWS Certified Data Analytics Professional, AWS Certified Database Specialty, CDMP).
• Experience with data visualization and business intelligence tools (e.g., Tableau, Power BI, Qlik).
• Snowflake SnowPro certification is a plus.
• Knowledge of machine learning and predictive analytics concepts and techniques.
• Familiarity with agile methodologies and DevOps practices in the context of data architecture and engineering.

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