Senior Data Engineer (MTS3)

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"