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Metro location, broad skill requirements, and a commonly pursued Data Engineer title increase competition.
Core data engineering skills are transferable across industries, though industrial data domain knowledge is beneficial.
Explicit seven-plus years and mandatory hands-on data pipeline and cloud experience make filters stringent.
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Design, build, and maintain scalable, high-performance data pipelines and infrastructure for real-time and historical industrial data sources.
Define and implement DevOps frameworks including CI/CD for data systems with monitoring and continuous improvement.
Collaborate with AI/ML engineers, data scientists, data architects, and cross-regional teams to enable analytics, forecasting, and anomaly detection use cases.
Bachelor’s or Master’s Degree in Computer Science or related field.
At least 7 years of professional experience in data engineering focusing on data sets and pipeline development.
Proficient in Python/PySpark and expert level SQL skills with relational databases.
Experience with Cloud platforms (AWS, Azure, or GCP), Data Lake/Big Data projects, and implementing data pipelines with DevOps practices.
Experienced in building and optimizing end-to-end data pipelines and data transformation solutions at scale.
Familiar with Agile (SCRUM) methodologies and cross-functional collaboration involving multiple stakeholders globally.
Strong technical consultant mindset capable of independent delivery and addressing complex data engineering challenges.