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

As a Lead Data Ops Engineering - Data Sourcing at Statista, your key responsibilities include leading a dedicated team of Data Engineers focused on leveraging AI tools for data extraction and processing. You will also contribute to strategic decisions regarding data production, tech stack initiatives, and the overall objective setting for your team. In addition, you will coordinate with various stakeholders to ensure the timely delivery of sourced data and automated processes.

To qualify for the Lead Data Ops Engineering - Data Sourcing role at Statista, candidates should have a minimum of three years of experience in Data Engineering or a similar field. Proven experience in leading teams and a startup-like motivation to drive growth are essential. Technical expertise in tools like Python, SQL, AWS, and automation frameworks is also an important requirement.

At Statista, we are dedicated to the continuous growth and development of our employees. As a Lead Data Ops Engineering - Data Sourcing, you will have access to numerous career and training opportunities, allowing you to enhance your skills and advance your career. Additionally, our international team fosters a culture of learning through collaboration and shared experiences.

Statista promotes a vibrant and inclusive work culture. As a Lead Data Ops Engineering - Data Sourcing, you will work within a dynamic environment that values diversity, encourages creative thinking, and offers flexible working arrangements. Our team enjoys a supportive atmosphere enriched with social events and a shared mission of empowering people with data.

In the Lead Data Ops Engineering - Data Sourcing position at Statista, you will primarily work with an advanced tech stack that includes Python, PostgreSQL, Docker, and AWS, along with automation tools like Apache Airflow. Mastery in these technologies is vital for effectively leading your team in executing data sourcing and production responsibilities.

Prioritizing tasks as a Lead Data Ops Engineering involves assessing the urgency and significance of each project, balancing long-term goals with immediate deadlines. Establishing clear communication with stakeholders and regularly reviewing team objectives helps in managing workload effectively.

In your response, emphasize specific AI tools you’ve used in your previous roles, such as BeautifulSoup or Selenium, and discuss how you implemented them to enhance data extraction processes. Highlight your success stories and the impact these tools had on project outcomes.

To lead a diverse team effectively, it’s important to foster an inclusive environment where every team member feels valued. Implementing regular check-ins, encouraging open discussions, and leveraging the unique strengths of team members are strategies that can help maintain a cohesive team dynamic.

Share specific challenges such as scaling data processes or dealing with data inconsistencies and detail the steps you took to handle those situations. Discussing the successful outcomes and lessons learned will showcase your problem-solving abilities.

To ensure data accuracy, implementing robust validation checks and data cleaning practices is vital. Describe methodologies you’ve used, such as automated tests or regular audits, to verify data integrity throughout your projects.

Discuss your passion for technology and how it drives you to innovate and enhance processes in a fast-paced environment. Your enthusiasm for problem-solving and exploring new technologies should resonate in your answer.

When managing conflicts within a team, I prioritize open communication and actively listen to each party involved. Facilitate a discussion to address issues and work towards a resolution that aligns with team goals and company values.

Share the resources you utilize for continuous learning, such as attending workshops, webinars, reading related articles, or participating in professional networks, and explain how you apply new knowledge to your work.

In your answer, detail a specific project involving automation, discussing the tools you used, the challenges you faced, and the outcomes achieved. Focus on how automation improved efficiency and reduced manual effort in your team's workflow.

Collaboration is crucial in Data Operations. Discuss how you encourage teamwork among data engineers and other departments to achieve common goals, and the tools or practices you use to enhance collaborative efforts.