As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

MAIN PURPOSE OF ROLE

The Staff Data Engineer/Architect is responsible for designing, implementing, and maintaining an organization's databases, data warehousing systems, and data processing architectures. They play a crucial role in managing and safeguarding the company's data, developing strategies for data acquisitions, and optimizing data retrieval, ensuring all data systems meet company and client needs.

MAIN DUTIES

• The following is a non-exhaustive list of responsibilities and areas of ownership of the Staff Data Engineer:
• Evaluate and enhance the performance of data management systems, ensuring they meet both technical and business requirements.
• Develop and implement data governance policies and procedures to ensure the integrity, confidentiality, and availability of enterprise data.
• Constantly in coordination with cross-functional teams, such as software developers, data scientists, and IT staff, to ensure data solutions are optimized and aligned with company goals.
• Liaise with globally based business teams and local project management teams to understand their data needs and provide suitable solutions.
• Lead the data architecture vision and strategy for the organization, often presenting to stakeholders and executive teams.
• Review existing systems and processes, identifying areas for improvement and implementing changes in a timely and effective manner.
• Operates with a high degree of independence but also collaborates extensively with various teams and stakeholders within the organization to ensure the effectiveness of data systems and strategies.
• Actively stay abreast of the latest industry developments, technologies, and best practices in data management, participating in professional development opportunities as needed.
• Responsible for mentoring and guiding junior data engineers and other technical staff, promoting a culture of continuous learning and improvement within the team.

REQUIREMENTS

• Education 
• Bachelor's or Master's degree in Computer Science, or similar discipline (or equivalent experience)
• Technical Skills and Experience
• Minimum 8 years as a Senior data engineer or higher in a professional environment
• Data Architecture: Proficiency in designing, implementing, and maintaining scalable and robust data architectures. This includes understanding of data warehousing, data lakes, and databases.
• Understanding Architecture Tradeoffs: Ability to evaluate and articulate the tradeoffs of different architectural solutions. This includes considerations of cost, performance, scalability, reliability, and security.
• Machine Learning Pipelining: Expertise in designing and implementing data pipelines for Machine Learning. This involves understanding of data extraction, transformation, and loading (ETL) processes, as well as feature engineering and model deployment. Understanding of how data quality impacts the performance of these algorithms.
• Data Modeling and Design: Expertise in logical and physical data modeling, with the ability to design optimized data structures for Machine Learning applications.
• Technical Skills: Proficiency in tools and technologies used for managing big data (like Hadoop, Spark) and developing Machine Learning models (like Python, R, TensorFlow).
• Analytical and Problem-Solving: Ability to analyze complex data, identify patterns, and solve problems. This includes troubleshooting issues in data pipelines and optimizing them for better performance.
• Ability to clearly articulate complex data architecture concepts and tradeoffs to both technical and non-technical stakeholders.
• Ability to manage and prioritize multiple projects, often with tight deadlines.
• Keeping up-to-date with the latest trends and advancements in data architecture and Machine Learning, and applying this knowledge to improve existing systems and processes.