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Lead, Data Engineer

Lead, Data Engineer

Mastercard

5 - 9 years

0 Lacs

pune maharashtra

Posted:1 day ago| Platform: Shine logo

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Skills Required

python aws azure gcp glue git airflow dbt docker pyspark s3 data factory databricks cicd agilescrum step functions terraform

Work Mode

On-site

Job Type

Full Time

Job Description

As the Lead Data Engineer at Mastercard, you will be responsible for designing and building scalable, cloud-native data platforms using PySpark, Python, and modern data engineering practices. Your role will involve mentoring and guiding other engineers, fostering a culture of curiosity and continuous improvement, and creating robust ETL/ELT pipelines to serve business-critical use cases. You will lead by example by writing high-quality, testable code, participating in architecture and design discussions, and decomposing complex problems into scalable components aligned with platform and product goals. Championing best practices in data engineering, you will drive collaboration across teams, support data governance and quality efforts, and continuously learn and apply new technologies to improve team productivity and platform reliability. To succeed in this role, you should have at least 5 years of hands-on experience in data engineering with strong PySpark and Python skills. You should also possess solid experience in designing and implementing data models, pipelines, and batch/stream processing systems. Additionally, you should be comfortable working with cloud platforms such as AWS, Azure, or GCP and have a strong foundation in data modeling, database design, and performance optimization. A bachelor's degree in computer science, engineering, or a related field is required, along with experience in Agile/Scrum development environments. Experience with CI/CD practices, version control, and automated testing is essential, as well as the ability to mentor and uplift junior engineers. Familiarity with cloud-related services like S3, Glue, Data Factory, and Databricks is highly desirable. Furthermore, exposure to data governance tools and practices, orchestration tools, containerization, and infrastructure automation will be advantageous. A master's degree, relevant certifications, or contributions to open source/data engineering communities will be considered a bonus. Exposure to machine learning data pipelines or MLOps is also a plus. If you are a curious, adaptable, and driven individual who enjoys problem-solving and continuous improvement, and if you have a passion for building clean data pipelines and cloud-native designs, then this role is perfect for you. Join us at Mastercard and be part of a team that is dedicated to unlocking the potential of data assets and shaping the future of data engineering.,

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Lead, Data Engineer