Senior Engineer 1

As a Senior Data Engineer - Platform at Live Nation Entertainment, your responsibilities include enhancing the performance and reliability of our data platform services, designing and building scalable onboarding and automation capabilities, and ensuring data quality across all systems. You will also troubleshoot data platform issues and participate in on-call rotations for support, all while collaborating with cross-functional teams to deliver data solutions that create real value for the organization.

To qualify for the Senior Data Engineer - Platform position at Live Nation Entertainment, you should have strong skills in Python or PySpark, a good understanding of data lakes and data warehouses, and experience with version control systems like Git. Knowledge of SQL, experience in cloud services such as AWS or GCP, and familiarity with data ingestion services and techniques are essential. Additionally, experience with workflow automation tools like Airflow and visualization tools is a plus.

The Senior Data Engineer role is crucial to the Core Data Services team at Live Nation Entertainment. By developing and maintaining data systems, ensuring data quality and integrity, and automating data processes, you will significantly enhance the team's ability to empower data-driven insights. Your work will directly influence how data is managed and utilized across the organization, supporting our mission of creating innovative and valuable data solutions.

A successful Senior Data Engineer - Platform at Live Nation Entertainment should have expertise in Python and PySpark for data engineering tasks, a solid understanding of both ANSI SQL and Spark SQL, and experience with data ingestion services. Familiarity with cloud platforms like AWS, Google Cloud, or Azure, as well as knowledge of tools for workflow automation, is essential. Experience with diverse data formats and visualization tools would be advantageous.

As a Senior Data Engineer - Platform at Live Nation Entertainment, you will thrive in an agile and collaborative environment. You will work alongside passionate team members in a dynamic setting focused on data innovation and improvement, where everyone's contribution is valued. The culture encourages resourcefulness, creativity, and the ongoing pursuit of knowledge in the ever-evolving field of data engineering.

To ensure data quality and integrity, I would implement rigorous data validation processes, leverage automated quality checks, and establish clear guidelines for data entry and modification. Regular audits and monitoring data pipelines can also help identify inconsistencies or errors early in the process.

In my previous roles, I have worked extensively with AWS and Google Cloud, utilizing services such as AWS S3 for data storage, AWS Lambda for executing code in response to events, and Google BigQuery for data warehousing. I have designed and deployed scalable data solutions that effectively leverage these platforms for high availability.

I start by analyzing existing data flow for bottlenecks or redundancies. Implementing parallel processing tasks and adjusting the infrastructure for better resource utilization are key steps. Regular maintenance, including updates to datasets and workflows, is vital in maintaining peak performance.

I have leveraged Airflow to streamline workflows by scheduling and monitoring data pipelines, which helps automate repetitive tasks. By defining dependencies and scheduling jobs, I've improved operational efficiency and reduced execution times significantly.

I tackled a complex issue involving inconsistent data formats across multiple sources. I developed an ETL pipeline that standardized these formats using Python libraries, enabling a unified data model for analysis and reporting, which greatly improved downstream data processing efficiency.

I keep up with trends by attending industry conferences, participating in online courses, and following thought leaders in data engineering on social media and platforms like Medium. This helps me identify new tools and methodologies that could enhance our data strategies.

Collaboration has been key in my previous roles; I regularly engaged with data analysts to understand their requirements while also working alongside product teams to ensure our data solutions met their needs. These interactions have proven crucial in aligning our goals and ensuring successful project outcomes.

I typically utilize logging to trace errors and understand their context. Integrating monitoring tools helps catch issues early. Additionally, I break down complex queries into simpler components to isolate potential problems holistically.

I prioritize tasks based on deadlines and their impact on overall projects. Using project management tools to track tasks and communicate with stakeholders allows me to adjust priorities quickly while ensuring transparency and accountability.

I have experience using visualization tools like Tableau and Power BI to convert raw data into actionable insights. I believe effective data storytelling is essential for stakeholders and aim to present data clearly to facilitate decision-making.