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Metro location and broad, generalist data skillset increase competition despite seniority and mid-tier employer.
GCP, Python, Kafka, and data platform skills are broadly transferable across industries.
Explicit 13–16 years requirement plus mandatory GCP, Kafka, and leadership makes screening highly stringent.
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Design and architect scalable data platforms, data pipelines, and implement batch processing frameworks using Spring Batch and related technologies.
Lead the Data Engineering POD including driving architecture decisions, design reviews, mentoring engineers, and removing technical blockers to ensure smooth delivery.
Design and deploy GCP resources using Infrastructure as Code, collaborate with multiple teams to deliver solutions, and ensure data quality, failure handling, and monitoring strategies are in place.
13–16 years of relevant work experience.
Strong hands-on experience with Data Engineering Architecture and solution design including batch processing, data ingestion, transformation, and integration.
Proficiency with Python, Spring Batch, Kafka, Google Pub/Sub, GCP (Google Cloud Platform), and Infrastructure as Code tools such as Terraform.
Strong leadership experience with POD/technical leadership and stakeholder management.
Experienced in leading data engineering teams/PODs driving architecture and technical best practices in cloud environments, especially GCP.
Skilled in designing end-to-end data pipelines focusing on scalability, automation, and fault tolerance with knowledge of data governance and quality.
Comfortable operating cross-functionally, mentoring engineers, and managing complex data platform deliveries within agile teams.