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Tier-1 brand plus mid-level generalist data role with common skills drives high competition.
Core data engineering skills are broadly transferable across industries, so low background sensitivity.
Multiple mandatory technologies and explicit 5+ years experience enforce high technical shortlisting strictness.
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Lead design, development, and maintenance of scalable cloud-based data processing pipelines and infrastructure with adherence to engineering standards and governance.
Architect and optimize large-scale data models focusing on storage efficiency, high-performance retrieval, and advanced analytics while ensuring data integrity and quality.
Drive data strategy execution, data quality initiatives, and alignment of data engineering solutions with business objectives across cross-functional teams.
5+ years of applied software engineering experience with formal training or certification.
Expertise in distributed data processing frameworks (Spark), cloud data lakehouse platforms (AWS Data Lake services or Databricks), and scheduling/orchestration tools (Airflow, AWS Step Functions or similar).
Proficiency in programming languages including Java or Python, Python, SQL, and at least one additional language (e.g., Java or Scala).
Experience with microservices architecture, serverless computing, containerization tools (Docker, Kubernetes), and data modeling techniques (Dimensional, Data Vault, Kimball, Inmon).
Experienced leader in building and scaling cloud-based data engineering solutions aligned with business goals and governance frameworks.
Skilled in incorporating AI-assisted development practices for code quality and delivery improvements while ensuring secure and compliant use of AI in engineering workflows.
Proficient in organizing collaborative technical sessions and architecting reusable design patterns to promote innovation and excellence in data engineering.