Data Engineer

Job Description

Job Title: Principal Data Engineer (MTS4 / Principal Engineer) About the Role As a Principal Data Engineer, you will drive the strategy, architecture, and execution of large-scale data solutions across our function. This role involves tackling highly ambiguous, complex challenges where the business problem may not be fully defined at the outset. You will partner closely with cross-functional teams (Engineering, Product, Operations) to shape and deliver our data roadmap. Your work will have a profound impact on our functions' data capabilities, influencing multiple teams’ technical and product direction. You should bring deep expertise in designing and developing robust data pipelines and platforms, leveraging technologies such as Spark, Airflow, Kafka, and other emerging tools. You will set standards and best practices that raise the bar for engineering excellence across the organization. Key Responsibilities Architect & Define Scope Own end-to-end design of critical data pipelines and platforms in an environment characterized by high ambiguity. Translate loosely defined business objectives into a clear technical plan, breaking down complex problems into achievable milestones. Technology Leadership & Influence Provide thought leadership in data engineering, driving the adoption of Spark, Airflow, Kafka, and other relevant technologies (e.g., Hadoop, Flink, Kubernetes, Snowflake, etc.). Lead design reviews and champion best practices for coding, system architecture, data quality, and reliability. Influence senior stakeholders (Engineers, EMs, Product Managers) on technology decisions and roadmap priorities. Execution & Delivery Spearhead strategic, multi-team projects that advance the organization’s data infrastructure and capabilities. Deconstruct complex architectures into simpler components that can be executed by various teams in parallel. Drive operational excellence, owning escalations and ensuring high availability, scalability, and cost-effectiveness of our data solutions. Mentor and develop engineering talent, fostering a culture of collaboration and continuous learning. Impact & Technical Complexity Shape how the organization operates by introducing innovative data solutions and strategic technical direction. Solve endemic, highly complex data engineering problems with robust, scalable, and cost-optimized solutions. Continuously balance short-term business needs with long-term architectural vision. Process Improvement & Best Practices Set and enforce engineering standards that elevate quality and productivity across multiple teams. Lead by example in code reviews, automation, CI/CD practices, and documentation. Champion a culture of continuous improvement, driving adoption of new tools and methodologies to keep our data ecosystem cutting-edge. Qualifications Education & Experience : Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent experience). 5+ 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. Show more Show less