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Tier-1 brand, mid-level generalist data role, metro location, and broad skill requirements increase competition.
Core data engineering skills (Spark, Azure, ETL, SQL) are easily transferable across industries.
Explicit 4–7 years requirement plus mandatory Spark, Azure/Databricks, Python/PySpark and ETL experience increases strictness.
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Develop and maintain scalable data pipelines and new API integrations to handle increasing data volumes and complexity.
Connect offline and online data sources to enhance understanding of customer behavior and personalization efforts, including data pre-processing and visualization.
Ensure data quality and governance by cleansing data, implementing standards, and enabling accessible, timely data use across the enterprise.
Bachelor's or Master's degree in Engineering, Computer Science, Math, Statistics, or equivalent.
4-7 years of relevant work experience including stakeholder management experience considered a plus.
Strong programming skills in Python/PySpark/SAS and experience with large data sets using Hadoop, Hive, Spark optimization.
Experience with cloud platforms (preferably Azure) and related services (Azure Data Factory, ADLS Storage, Azure DevOps), plus knowledge of DevOps tools (Docker, CI/CD, Kubernetes, Terraform).
Experienced in enterprise data pipelines and ETL tools such as Ab Initio, Informatica, or DataStage with strong SQL and data modeling skills.
Familiar with cloud technologies and data engineering certifications, with a background working in Agile environments.
Capable of managing complex data transformation tasks and collaborating with stakeholders to improve data-driven decision processes.