Technical Lead

The Technical Lead at WebMD Health Corp. will be responsible for architecting and developing multiple data engineering projects across heterogeneous data sources. This includes producing and consuming data from messaging queues like Kafka and pushing/pulling data via REST APIs. Additionally, you will support and enhance the in-house Data Integration Framework, ensuring efficient data replication and profiling. Engaging with third-party data providers and translating complex technical subjects for a varied audience are also key components of the role.

To apply for the Technical Lead role at WebMD, you should have over 7 years of experience using data integration tools, along with traditional databases such as Postgres, MSSQL, and Oracle. Experience with MPP databases like Vertica or Google BigQuery, as well as in creating Entity-Relationship and Dimensional Data Models, is essential. A Bachelor’s degree in Computer Science or a related engineering field is also required, and familiarity with scripting languages and the Hadoop ecosystem is preferred.

Candidates applying for the Technical Lead position at WebMD should possess at least 7 years of experience using Data Integration Tools such as Pentaho or Talend. Furthermore, having demonstrated experience in managing traditional and MPP databases, along with leading a team of engineers, is crucial. Knowledge of ETL/ELT strategies and scheduling/orchestration tools will also be highly beneficial.

As a Technical Lead at WebMD, you will work on diverse data engineering projects that involve designing and supporting data frameworks, enhancing data integration methods, and solving complex data challenges within the healthcare sector. You will be actively engaged in innovative solutions that drive efficiency and effectiveness in health information services.

WebMD is dedicated to the professional growth of its Technical Leads by fostering a collaborative work environment and providing ongoing training opportunities. The position encourages engagement with various business units to enhance cross-functional knowledge, ensuring that Technical Leads are fully supported in their roles as technical advisors and team leaders.

When designing an ETL process as a Technical Lead, it's essential to first understand the data requirements and the sources of data. A solid strategy should include deciding on the transformation rules, data quality checks, and the necessary scheduling or orchestration tools to ensure timely execution. Always consider scalability and performance optimization to enhance overall efficiency.

Discuss your hands-on experience with Oracle or MSSQL, highlighting specific projects where you have optimized queries or created complex data models. Emphasize how your work improved data retrieval speed or ensured data integrity, showcasing any relevant tools or methodologies used during these projects.

Leading a data engineering team can come with several challenges such as varying skill levels among team members, tight deadlines, and scope changes. Share a specific example of a challenge you've faced, the actions you took to resolve it, and how your leadership helped guide the team to success, focusing on communication and collaboration.

To ensure data quality in ETL processes, implementing data validation checks and profiling during each phase of the process is essential. Discuss techniques for tracking data inconsistencies and errors, and highlight how continuous monitoring and feedback loops have improved quality in your previous implementations.

Describe your familiarity with cloud databases such as Google BigQuery, emphasizing any projects where you utilized its capabilities for scalable data processing and querying. Highlight the specific use cases you handled and how it contributed to efficient data handling or analytics.

To effectively communicate technical concepts to non-technical stakeholders, I use simple, jargon-free language, supported by visual aids whenever possible. Explain how breaking down complex issues into relatable scenarios or examples helps bridge the understanding gap between technical and non-technical team members.

Staying updated with emerging technologies in data engineering requires a multifaceted approach, including participating in online training courses, attending industry conferences, and engaging with tech communities through forums and networking. Share specific examples of how you've incorporated new tools or methodologies into your workflow.

Illustrate an instance where you've enhanced the performance of an ETL process, detailing the methods you employed, such as optimizing SQL queries, modularizing data transformation steps, or improving resource allocation. Discuss the results you achieved in terms of time savings and resource utilization.

Discuss your experience implementing Agile practices in data engineering projects, focusing on how you've leveraged sprints, stand-ups, and retrospectives to improve collaboration and responsiveness to changes. Provide an example where Agile methods led to quicker project delivery or better stakeholder satisfaction.

Mentoring junior engineers involves providing hands-on guidance and fostering an open environment for questions. Discuss techniques for conducting regular check-ins, setting development goals, and complementing on-the-job training with formal learning opportunities to build a strong knowledge foundation.