Data Architect

As a Data Architect at Velotio Technologies, your main responsibilities will include leading the technical delivery for large enterprise customers throughout their lifecycle, from discovery to implementation and ongoing support. You'll design and build complex, streaming data pipelines using our proprietary ETL engine, collaborate with Data Scientists to implement advanced analytics, and analyze key customer use cases to devise common data solutions. Additionally, you'll drive adoption of these solutions through validation exercises and create data dictionaries and process flow diagrams.

To qualify for the Data Architect position at Velotio Technologies, candidates should have 6 to 9 years of experience as a Data Architect. A solid understanding of batch and streaming data processes is desirable, along with strong analytical abilities and problem-solving skills. Familiarity with programming languages such as Python, Java, or R, and experience with SQL or NoSQL databases will be advantageous. Additionally, being comfortable working with customers to understand their concerns and suggest best practices is essential.

A Data Architect at Velotio Technologies should be familiar with various technologies including Python, Java, Git, and data analysis libraries like Pandas. Experience with both SQL and NoSQL databases is important. Knowledge of ETL pipelines and data mining techniques will be essential in tackling the complex data challenges you'll encounter in this role, particularly in a manufacturing environment.

At Velotio Technologies, we foster an environment where our Data Architects can thrive through autonomy and empowerment. Our flat hierarchy promotes fast decision-making, allowing you to take ownership of projects and accelerate your growth. We also celebrate successes regularly, creating a strong and positive culture. Plus, we prioritize inclusion and diversity, ensuring that every team member feels valued and supported in their professional development.

The company culture at Velotio Technologies is characterized by a strong, fun, and positive environment where innovation flourishes. As a Data Architect, you'll find an autonomous work culture that encourages ownership and collaboration with others. We focus on inclusivity and celebrate our diversity, ensuring that every employee, regardless of background, feels recognized and appreciated for their contributions.

When answering this question, be specific about the ETL tools and technologies you've used. Cite specific projects where you designed complex ETL pipelines that pulled data from various sources. Discuss your approach to ensuring data accuracy and efficiency in these processes.

To effectively address this question, emphasize your methodological approach to diagnosing data quality issues. Provide examples of how you've used data profiling and validation techniques, mentioning any specific tools you've utilized to resolve these challenges.

Highlight your communication skills and your ability to translate technical information to non-technical stakeholders. Discuss past collaborations, focusing on how you aligned data architecture with the Data Scientists' analytics goals. Mention specific projects where collaboration led to successful outcomes.

In response, share your knowledge of machine learning fundamentals and specific experiences where you implemented ML models. Discuss how you integrated these models into data pipelines or architecture and the impact they had on data-driven decision-making.

Clearly describe the differences between streaming and batch data processes. Illustrate with examples from your experience where appropriate. Discuss their advantages, potential use-cases, and the scenarios in which you've worked with each type of data.

Demonstrate your problem-solving process step by step. Discuss how you assess problems to identify the root cause, tools you employ for data analysis, and strategies for testing and implementing solutions. Providing a real example will strengthen your response.

Detail your proficiency with various SQL and NoSQL databases you've worked with, highlighting specific projects where you applied your skills. Talk about the scenarios where each type of database was preferable and how you've optimized database performance in those instances.

Focus on your experience with designing flexible and scalable data architectures that accommodate growth. Highlight best practices for modeling data structures and any techniques you use to test scalability, including performance testing methods you've employed in past projects.

Share a specific example where effective communication was vital. Describe the situation, what you conveyed, and how you tailored the message to your audience. Emphasize techniques you employed to simplify technical jargon and foster understanding.

Convey your passion for the field by discussing your commitment to continuous learning. Mention resources you use to stay informed about trends, such as industry publications, webinars, or professional networks. Highlight how you apply this knowledge to enhance your work.