Senior Developer/Tech Lead

As a Senior Developer/Tech Lead, your responsibilities will include managing a team of ETL developers, overseeing task assignments, and ensuring that projects and proofs of concept are delivered on time. You'll also provide technical leadership, guiding your team in developing complex ETL mappings and optimizing processes for efficiency. Keeping up with the latest trends in ETL technologies is essential to maintain our competitive edge.

To qualify for the Senior Developer/Tech Lead role, candidates should have at least a Bachelor’s degree in computer science or a related field. Additionally, at least 7 years of relevant experience in designing and implementing ETL processes is necessary, along with expertise in tools like Azure Data Factory, SQL Server Integration Services, and data visualization tools such as Power BI or Tableau.

A Senior Developer/Tech Lead should be well-versed in a variety of ETL tools and technologies, including but not limited to Oracle Data Integrator (ODI), Azure Data Factory (ADF), SQL Server Integration Services (SSIS), Talend, and Alteryx. Familiarity with these tools will ensure effective management and development of robust ETL processes.

Team management is a critical aspect of the Senior Developer/Tech Lead role. You will be responsible for assigning tasks and mentoring team members to promote their growth and ensure project success. Strong interpersonal and communication skills are essential for effectively leading and inspiring your team.

The career progression for a Senior Developer/Tech Lead typically involves moving into higher-level management roles, such as Engineering Manager or Director of Technology, where you could take on more strategic responsibilities. Continuous learning and staying updated on emerging ETL trends will be crucial for advancing your career in this dynamic field.

When answering this question, focus on specific projects where you utilized Azure Data Factory, detailing the complexities you faced and how you overcame them. Highlight any optimizations you implemented to improve process efficiency.

Discuss various optimization techniques that you've used, such as indexing, partitioning, or parallel processing. Provide examples from past experiences that demonstrate your ability to assess and refine ETL operations for better performance.

Talk about methods you apply to maintain data integrity, such as implementing ETL control tables, error logging, and auditing practices. Emphasize the importance of setting up data quality checks throughout the ETL workflow.

Provide a specific example where you took charge, outlining the project's challenges, your leadership approach, and the outcome. Your answer should reflect your ability to motivate and guide team members effectively.

Explain the systematic approach you take when troubleshooting ETL failures, such as analyzing logs, isolating issues, and employing debugging techniques. Discuss how you communicate issues with your team to foster collaboration.

Share methods you use to stay updated, such as attending industry conferences, participating in online forums, or engaging in continuous professional training. This demonstrates your commitment to lifelong learning in the tech industry.

Discuss critical metrics like data processing time, error rates, and resource utilization. Mention how you use these metrics to assess the effectiveness of your ETL processes and identify areas for improvement.

Illustrate your methodology in creating BI reports, emphasizing collaboration with stakeholders to understand requirements and using tools like Power BI or Tableau to visualize data effectively. Highlight any successful projects you've completed.

Describe your conflict-resolution strategy, emphasizing the importance of communication, empathy, and finding common ground. Provide an example of how you successfully resolved a team conflict in the past.

Explain how you value thorough documentation of ETL processes for future reference, team onboarding, and compliance. Discuss how this practice enhances project efficiency and aids in maintaining data quality.