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Mid-level role, metro location, popular data-engineer title, and broad cloud/big-data requirements increase competition.
Core data engineering skills are highly transferable across industries.
Explicit 3–6 years plus mandatory cloud, Redshift, Spark, and ETL experience indicates high filtering.
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Own the design, build, and maintenance of scalable data pipelines and ETL/ELT workflows supporting batch and near real-time data processing.
Lead onboarding and integration of newly acquired brands into the data platform, standardizing migration frameworks.
Manage and optimize data infrastructure on cloud platforms (AWS), ensuring data reliability, performance, and secure access across teams.
3–6 years of hands-on experience in data engineering or backend data systems.
Bachelor’s degree in Computer Science, Engineering, or related field.
Strong proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL).
Experience with AWS cloud data platforms, including AWS Glue, Amazon S3, and Redshift.
Experienced in building and operating scalable data infrastructure and pipelines during integrations and acquisitions.
Proficient in Python programming and familiar with big data processing frameworks like Apache Spark.
Comfortable collaborating with cross-functional teams including product managers, analysts, and data scientists to deliver business-ready datasets.