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Metro location and a common Data Engineer title increase competition despite seniority and niche skills.
Core data engineering skills transfer across industries, but enterprise IoT and domain specifics create moderate specificity.
Explicit 18–20 years, many mandatory technologies and leadership expectations make hiring filters highly strict.
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Lead design, development, and industrialization of reusable, industrial-grade data pipelines and data products using cloud technologies.
Set and drive technical best practices for data engineering across teams, ensuring pipeline performance, maintainability, and quality through code reviews and addressing technical debt.
Manage and mentor data engineering experts, plan production operations, and engage with business stakeholders and external forums for capability development and advisory roles.
18-20 years of hands-on experience in data engineering and software engineering.
Master's or PhD in computer science, software engineering, statistics, mathematics, or data science/machine learning.
Technical expertise with Databricks, AWS Glue/EMR/Athena, Airflow, DBT, Spark, Kafka, Terraform, Git-based tools; certifications in Databricks and AWS preferred.
Proven experience in building cloud-based, production-grade data pipelines, implementing DevSecOps/DataOps, and adherence to cybersecurity, data privacy, and compliance standards.
Strong background in designing scalable data products with deep expertise in cloud data engineering and industrializing data pipelines for enterprise use cases.
Experienced in leading and mentoring cross-cultural global teams, implementing agile methodologies and maturing DataOps practices.
Effectively translates complex business requirements into technical solutions, while proactively driving innovation and operational excellence in data engineering.