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Tier-1 brand, mid-level generalist data engineer role, and widely applicable skillset increase applicant competition.
Core data engineering skills are broadly transferable across industries, though payments-specific governance adds domain tilt.
Explicit 2.5–4 years plus specific PySpark/Hadoop/SQL and data governance requirements raise filtering rigor.
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Design, build, and operate scalable ETL/ELT data pipelines and curated datasets on big data platforms such as Hadoop and cloud environments.
Ensure data quality, governance, and reliability for analytics products, reporting, and advanced modeling through automation and monitoring.
Collaborate with Product, Data Science, and Platform teams to translate requirements into reusable data models and support AI/ML data needs.
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
2.5 to 4 years of relevant experience in data engineering or big data analytics engineering.
Proficiency in PySpark, Python, SQL, and experience with Hadoop ecosystem tools (e.g., HDFS, Hive, Impala) and orchestration tools (e.g., Apache Airflow).
Experience with data modeling, incremental processing (CDC, SCD Type 1/2), and building curated datasets; knowledge of data governance and security requirements.
Experienced in designing and optimizing large-scale, high-performance data pipelines leveraging distributed computing patterns.
Able to implement robust data quality frameworks and automate testing and CI/CD pipelines.
Familiar with preparing data for AI/GenAI consumption, including handling semi-structured/unstructured data with focus on privacy and reproducibility.