Okay, I've searched for Azure Data Engineer roles in India that are remote, require 3-5 years of experience, involve Azure Data Factory skills, and are full-time positions. I have found 127 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Azure Data Engineer role is primarily designed to integrate and harmonize external data sources into a Fortune 50 client's data ecosystem, enabling robust reporting and analytics capabilities. The core responsibility lies in ingesting data from multiple complex platforms—such as Snowflake, Blue Yonder (a third-party logistics tool), and SAP S4 HANA—using Azure Data Factory (ADF) and Azure Databricks. This requires not only technical proficiency in cloud data engineering technologies but also the ability to optimize SQL queries and tune performance to handle large-scale data ingestion efficiently. The role demands familiarity with API-driven data ingestion, scripting in Python, PySpark, or Scala, and working knowledge of Agile/Scrum methodologies to collaborate in a dynamic, fast-paced development environment. Working hours indicate synchronization with global teams, requiring flexibility and ownership. Additionally, experience with data quality frameworks and reporting tools like Power BI hints at a commitment to end-to-end data integrity and visualization. Success in this role means delivering efficient, scalable pipelines that enable accurate, timely insights, while navigating the complexities of cross-functional coordination, varying data formats, and evolving business requirements. The candidate must also embody a proactive, can-do attitude with good interpersonal skills to effectively engage with stakeholders and internal teams.

Company Analysis:

Lensa is a technology-driven career platform that uniquely leverages machine learning to match job seekers with positions based on skills, personality, and preferences, elevating the traditional job search process into a more personalized and efficient experience. They position themselves as an innovator in the recruitment technology space, emphasizing inclusive and people-first values. Even though the Azure Data Engineer role is for a Fortune 50 client sourced through Insight Global, understanding Lensa’s culture reflects a strong focus on leveraging data, AI, and user-centric solutions, which is directly aligned with the technical demands of the role. The company’s emphasis on diversity, inclusion, and authentic employee engagement suggests a collaborative and respectful working environment. The engineering and data science teams are internationally distributed, indicating a modern, global workplace where communication across time zones is routine and agility in mindset is valued. For a candidate, this environment means being adaptable, having cultural sensitivity, and being driven by data-led innovation. The role fits strategically in supporting a large-scale, data integration project—likely a high-impact, high-visibility initiative expected to enhance the client’s enterprise data infrastructure and decision-making capabilities, thus serving as a critical growth and transformation vector.

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

• Behavioral: Tell me about a time when you had to manage conflicting priorities while working with multiple data sources. How did you ensure timely delivery without compromising quality?
• Behavioral: Describe an instance where you identified a performance bottleneck in a data pipeline. What steps did you take to resolve it?
• Behavioral: Can you share a situation where you had to collaborate closely with cross-functional teams in a Scrum environment? How did you contribute to the team’s success?
• Behavioral: How do you approach learning new tools or technologies that are unfamiliar but critical to your project objectives?
• Technical: Walk me through your experience designing and implementing data ingestion pipelines using Azure Data Factory. What were the key challenges and how did you address them?
• Technical: How do you optimize SQL queries for large-scale data warehouses like Snowflake, and what performance tuning techniques do you commonly use?
• Technical: Explain how you have used Azure Databricks and PySpark or Scala in processing large datasets. What strategies do you employ to maintain scalability and reliability?
• Technical: What is your approach to ensuring data quality throughout the ETL process, and what tools or frameworks (e.g., Apache Griffin, Deequ, Great Expectations) have you implemented?
• Situational: Imagine you receive data feeds from a new third-party system with inconsistent formats and missing documentation. How would you proceed to integrate this data reliably?
• Situational: You notice repeated failures in scheduled ADF pipelines that delay the availability of data for reporting. How would you investigate and resolve the issue?
• Situational: A key stakeholder requests a last-minute change to a data transformation logic that could delay the sprint deliverable. How do you handle this request?
• Company Fit: Why are you interested in working with Lensa and this Fortune 50 client on this Azure Data Engineer role?
• Company Fit: How do your personal values and work style align with Lensa’s emphasis on diversity, inclusion, and authentic engagement?
• Company Fit: Given Lensa’s focus on machine learning and personalized job matching, how do you see your skills contributing to such innovative environments?
• Questions for Interviewer: Can you describe the typical team structure and collaboration patterns for this data engineering project?
• Questions for Interviewer: How does this role directly impact the client’s business objectives and long-term data strategy?
• Questions for Interviewer: What are the biggest challenges the team currently faces with data ingestion and reporting, and how could someone in this role help overcome them?