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Tier-1 brand, popular lead engineer title, and broad data engineering requirements increase candidate competition.
Core data engineering skills are transferable across industries, though retail-specific ontology and BI experience add some bias.
Explicit 7–10 years plus many mandatory technologies and leadership expectations make screening highly selective.
Lead technical direction and design for a data product squad focused on consumption layers, APIs, and data pipelines.
Drive simplification and unification of data products to reduce operational and licensing costs while improving consistency and business context integration.
Own production reliability, scalability, security, and mentoring of junior engineers; contribute to cross-team architecture and platform toolkits.
7–10 years of backend and/or data engineering experience.
Deep expertise in Java, Python, or Scala; strong SQL and data modeling skills.
Hands-on experience with distributed systems, Databricks, Spark, Delta Lake, Airflow, and cloud platforms (AWS, GCP, or Azure).
Bachelor's or Master's degree in Computer Science, Information Systems, or related field, or equivalent experience.
Experienced technical leader with a proven track record in designing distributed data architectures and leading engineering teams.
Strong skills in API design, data contracts, engineering mentorship, and incident management in production environments.
Familiarity with advanced data governance, ontology/semantic layers, and large-scale BI/data analytics platforms preferred.