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Popular mid-level Data Engineer role, metro location, broad required skillset increases applicant competition.
Core data engineering skills transferable, but enterprise IoT and domain knowledge moderately limit cross-industry fit.
Mandatory cloud, ETL, Spark, Kafka and data modeling skills create stringent technical shortlisting filters.
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Lead design, development, and maintenance of scalable data pipelines and data products supporting analytics, operational reporting, APIs, automation, and GenAI use cases across multiple enterprise domains.
Design and implement data-as-a-product principles including scalable, governed, and discoverable data assets with metadata, lineage, quality controls, and documentation enabling enterprise-wide reuse.
Collaborate with cross-functional teams including Product Managers, Data Scientists, and business stakeholders to prepare AI-ready datasets and deliver trusted knowledge sources for advanced analytics and intelligent business solutions.
Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Analytics, or equivalent practical experience.
Intermediate experience in relevant discipline with strong hands-on development and support of enterprise data pipelines, data transformations, and data integration solutions using modern cloud data platforms, data warehouses, and ETL/ELT technologies.
Experience in data modeling, SQL, data quality validation, performance optimization, and scalable architectures supporting multiple consumer patterns including APIs, analytics, AI/ML.
Work Experience Required: Intermediate experience in a relevant discipline (exact years not explicitly mentioned).
Experienced in working within Agile cross-functional teams collaborating with Product Managers, Data Scientists, and Architects to deliver business outcomes.
Knowledgeable in Data-as-a-Product operating models including data cataloging, lineage, metadata management, certification, and governance.
Familiarity with advanced AI-related technologies/practices such as GenAI, AI/ML, vector databases, semantic search, and AI-ready data engineering supporting enterprise AI solutions.