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Metro location and common mid-senior data engineering title increase applicant density.
Core data engineering skills like ETL, Spark, and cloud are highly transferable across industries.
Explicit 7–10 years requirement plus mandatory Python, SQL, Spark and data platform experience enforces strict filtering.
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Lead and manage execution of data engineering projects independently, delivering technical solutions for business challenges in digital marketing, eCommerce, BI, and self-service analytics.
Design and build operational data platforms including ETL pipelines, data lakes, streaming data hubs, and support machine learning model deployment.
Interact regularly with C-level clients, mentor team members, and collaborate across functions in an agile and flat organization.
7-10 years of professional hands-on experience in data engineering and software engineering.
Must have strong experience with Python, SQL, distributed programming languages (preferably Spark), and data warehousing.
Experience with cloud platforms (Azure is a plus), data lakes, ETL workflows; familiarity with Unix/Linux systems, Bash, webservices/APIs, containers (Docker), version control (Git).
Good written and oral English communication skills.
Proven capability to independently lead complex data engineering projects with measurable business impact in digital or eCommerce domains.
Experienced in building scalable data platforms using Big Data, distributed processing, and data warehousing ecosystems.
Able to engage and communicate effectively with executive-level clients and guide multi-disciplinary teams within an agile, collaborative environment.