Lead Data Ops Engineering - Data Sourcing (crawling/scraping) (m/f/d)

As the Lead Data Ops Engineering - Data Sourcing at Statista, your main responsibilities will include leading a team of data engineers to extract and automate data sourcing processes, managing project deliveries, and ensuring the data is processed and stored efficiently. You'll also be implementing strategic decisions within our leadership to enhance our data value contributions across the organization.

To excel in the Lead Data Ops Engineering - Data Sourcing role at Statista, you should have a minimum of 3 years of experience in Data Engineering or Data Operations, along with proven team leadership skills. A strong background in data sourcing techniques and proficiency with technologies such as Python, SQL, and AWS will also be essential.

Statista commits to employee development by offering various career and training opportunities, aiming to empower its team members in the Lead Data Ops Engineering - Data Sourcing role. You can expect mentorship, skill enhancement through projects, and the chance to engage in social events that foster teamwork and professional growth.

The team in the Lead Data Ops Engineering - Data Sourcing position at Statista utilizes a modern technology stack, which includes Python, BeautifulSoup, Selenium, SQL (Postgres), Docker, AWS, and Airflow. This robust framework allows us to automate data extraction processes efficiently and effectively.

As a Lead Data Ops Engineering - Data Sourcing at Statista, you'll enjoy a range of benefits including the flexibility to work from abroad, hybrid working arrangements, and access to mental health support. The vibrant international team and opportunities for social engagements further enhance your experience within the company.

When answering this question, detail specific data sourcing methods you have employed, particularly those that involve automation and scraping. Discuss the tools and technologies you utilized, such as Python or BeautifulSoup, and the impact of these methods on your projects.

You can effectively highlight your organizational skills by explaining how you assess project deadlines, stakeholder needs, and resource availability. Mention any tools or methodologies you use, such as Agile or Kanban, to keep your team aligned and focused on high-priority tasks.

Addressing data quality is pivotal in data operations. Discuss methods such as data validation, regular audits, and automated testing you implement to maintain integrity in data processed by your team. Provide examples of challenges you've faced and how you overcame them.

In your response, reflect on your conflict resolution techniques. It's vital to demonstrate your ability to mediate differences through open communication and establishing a collaborative environment. Share an actual experience that exemplifies your approach.

When discussing a project, consider highlighting your leadership role, the project's objectives, your team's deliverables, and the results achieved. This showcases your ability to guide your team toward success while also addressing the unique challenges faced.

Emphasize both technical and interpersonal skills as essential for this role. Technical skills may include programming proficiency and a solid understanding of data operations, while interpersonal skills could focus on leadership, communication, and teamwork.

Discuss your dedication to continuous learning, such as following industry leaders on social media, participating in webinars, and engaging with data engineering communities. Highlight how you incorporate new knowledge into your work to keep your team's processes cutting-edge.

Talk about specific automation tools like Airflow, Selenium, or custom scripts you’ve created. Provide examples of how these tools have streamlined processes, reduced manual errors, or sped up data collection times, ultimately benefiting the organization’s efficiency.

It’s important to communicate your strategies for alignment, such as regularly reviewing key performance indicators, engaging with stakeholders, and adapting your team's objectives based on the evolving goals of Statista.

Illustrate your familiarity with data lakes by discussing your work in integrating it into data operations. Explain how data lakes facilitate scalable storage and processing, thus enabling efficient data sourcing and analytics.