Okay, I've searched for Data Automation Engineer roles in Irving, TX, full-time positions that require 5+ years of experience with Python and SQL, expertise in Azure cloud services, and familiarity with API test automation tools. I have found 47 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Data Automation Engineer role at Lorven Technologies is fundamentally designed to ensure the reliability, performance, and scalability of data pipelines within a cloud-centric ecosystem. This position requires a blend of strong programming skills, particularly in Python and SQL, and deep operational knowledge of Azure cloud services, highlighting the importance of building and maintaining efficient, automated data workflows. The role goes beyond mere coding — it demands optimizing complex queries, implementing rigorous data quality assurance, and collaborating across teams to translate business requirements into technical solutions. Candidates will face challenges such as diagnosing data bottlenecks, ensuring pipeline resiliency, and maintaining seamless CI/CD practices via tools like GitLab and Jenkins, which reflects the need for both technical acumen and proactive problem-solving. Success in this role not only means delivering robust data solutions but doing so with agility and precision, enabling the company to leverage data as a strategic asset in a dynamic environment. Furthermore, the emphasis on automation testing frameworks and API testing tools indicates a culture of quality and reliability, implying that the engineer must be detail-oriented and quality-driven while balancing fast-paced development demands.

Company Analysis:

Lorven Technologies, as represented through the Dice platform, operates at the intersection of tech talent matchmaking and enterprise technology solutions. Given its positioning as a provider recruiting for tech roles, the company likely values innovation, technical expertise, and agility. The role based in Irving, TX, suggests a location-specific operational focus perhaps tied to client engagements or regional data projects. Their emphasis on cloud-based data solutions and modern automation tools signals a forward-thinking environment that embraces emerging technologies and continuous improvement. The company's culture appears to blend mission-driven innovation with responsiveness to client and industry demands. For the Data Automation Engineer, this means functioning in a setting where cross-functional collaboration, rapid iteration, and technical excellence are prized. The visibility from collaborating across teams and working with cloud services suggests opportunities for growth and meaningful contribution, but also the need to navigate fast-evolving tools and expectations. This position likely supports strategic goals such as cloud migration, data modernization, and automation — critical drivers for a technology services company aiming to remain competitive and deliver high-quality client solutions.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Can you describe a time when you had to optimize a complex SQL query to improve performance? What approach did you take and what was the outcome?
• Behavioral: Tell me about a situation where you identified and resolved a critical data quality issue in a production data pipeline.
• Behavioral: Describe an experience collaborating with cross-functional stakeholders to gather data requirements. How did you ensure their needs were effectively translated into technical solutions?
• Behavioral: Share an example of when you had to quickly adapt to a new cloud technology or automation tool. How did you manage the learning curve?
• Technical: How would you design and implement an end-to-end automated data pipeline using Azure Data Factory and Python?
• Technical: Walk me through your experience with CI/CD pipelines using GitLab or Jenkins in data engineering projects. How did these tools improve your deployment process?
• Technical: What strategies do you employ to maintain data quality and integrity during automated data ingestion and processing?
• Technical: Explain how you would leverage API test automation tools like Postman or ReadyAPI to validate data workflows and integration points.
• Situational: Imagine a key data pipeline has failed in production during off-hours. How would you diagnose and resolve this issue to minimize downtime?
• Situational: You receive ambiguous requirements from a business stakeholder for a data solution. How do you approach clarifying and translating these into project specifications?
• Situational: Suppose a new data volume exceeds your current pipeline’s capacity and causes slow performance. What steps would you take to optimize and scale your solution?
• Company Fit: What interests you most about working with Lorven Technologies and their approach to cloud data solutions?
• Company Fit: How do your values and working style align with a fast-paced, innovation-driven environment like the one suggested for this role?
• Company Fit: Given the collaborative nature of this position, how do you typically build relationships and communicate effectively with technical and non-technical teams?
• Questions for Interviewer: Can you describe how the Data Automation Engineer role interacts with other teams like data science, software engineering, or product management here?
• Questions for Interviewer: What are the biggest challenges currently facing the data engineering team, and how does this role contribute to addressing them?
• Questions for Interviewer: How does Lorven Technologies support continuous learning and professional growth for engineers working with emerging cloud technologies?