At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.
As a Data Engineer (MTS3), you will work on large, impactful data engineering projects where both the business problem and architectural strategies may not be fully defined from the onset. You will use your expertise to clarify requirements, define scope, and break down complex projects into manageable milestones. Through deep technical leadership, you will influence key decisions around design, coding standards, and system architecture, while mentoring other team members and driving technical excellence.
Key Responsibilities
- Scope Definition & Problem-Solving
- Take ownership of projects with medium to high ambiguity, translating loosely defined business objectives into clear data engineering plans.
- Break complex tasks into milestones and deliverables, ensuring clarity and focus for the team.
- Influence & Collaboration
- Provide guidance on best practices in design, coding, system architecture, operational excellence, and data engineering standards.
- Collaborate across functions, advising peers, Engineering Managers, and Product Managers to drive optimal technology choices.
- Act as a role model for effective design reviews, setting high standards for code quality and performance.
- Execution & Delivery
- Design, develop, test, and deploy data solutions that meet high standards of quality and reliability.
- Lead large projects, owning architectural and technology decisions (HLD) to ensure scalability and maintainability.
- Coordinate with other data engineers and stakeholders so work can proceed in parallel, then reassemble into a cohesive, high-quality launch.
- Balance both tactical requirements and strategic vision, proactively identifying technical debt and simplifying existing code or data architectures.
- Actively mentor junior and mid-level engineers, guiding them on best practices and fostering their growth.
- Impact & Technical Complexity
- Drive meaningful impact on software architecture, dependencies, performance, and business outcomes.
- Solve complex data engineering challenges, implementing solutions that are extensible, maintainable, and performant.
- Make informed trade-offs between short-term business goals and long-term technical considerations.
- Process Improvement
- Lead and participate in efforts to streamline processes, reduce friction, and introduce mechanisms that improve team efficiency.
- Continuously optimize data engineering workflows, introducing automation and best practices that enhance overall productivity.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience).
- 4+ years of software/data engineering experience, with significant exposure to large-scale distributed systems.
- Technical Expertise:
- Demonstrated proficiency with Spark, Airflow, Kafka, and at least one major programming language (e.g., Python, Scala, Java).
- Experience with data ecosystem technologies such as Hadoop, Flink, Snowflake, Kubernetes, etc.
- Proven track record of architecting and delivering highly scalable data infrastructure solutions.
- Leadership & Communication:
- Ability to navigate and bring clarity in ambiguous situations.
- Strong cross-functional collaboration skills, influencing both technical and non-technical stakeholders.
- Experience coaching and mentoring senior engineers.
- Problem-Solving:
- History of tackling complex, ambiguous data challenges and delivering tangible results.
- Comfort making informed trade-offs between opportunity vs. architectural complexity"
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