PAR Technology - Data Engineer I
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Requirements
• 3+ years of experience as a Data engineering professional, developing scalable big data solutions • Degree in Computer Science, Engineering, or other related fields • Demonstrated strength in data modelling and SQL within a Lakehouse architecture • Knowledge of cloud technologies, preferably AWS • Knowledge of big data technologies and programming language (Python), relational databases (Postgres, MySQL), NoSQL (MongoDB), and streaming (Kafka, Spark) • Must have PySpark and Spark Streaming experience • Excellent software engineering background with high familiarity with the software development lifecycle • Familiarity with GitHub and version control best practices • Strong problem-solving skills with demonstrated rigor in building and maintaining a complex data pipeline • Good communication skills and ability to articulate complex concepts with thoughtful, actionable recommendations • Leverage AI tools to streamline data pipeline development, accelerate root-cause analysis, and automate routine data quality checks to improve engineering efficiency • Interview Process: • Interview Process: • Interview #1: Phone Screen with Talent Acquisition Team • Interview #2: Video interview with the Hiring Manager (via MS Teams) • Interview #3: Video interview (technical) with the Team (via MS Teams) • Interview #4: Video interview (technical) with the Team (via MS Teams)
Responsibilities
• Work with large data sets and implement sophisticated data pipelines with both structured and semi-structured data • Collaborate with stakeholders to design scalable solutions • Manage and optimize our internal data pipeline that supports marketing, customer success and data science • Focus on ingesting, storing, processing, and analyzing large datasets • Be a technical contributor to PAR’s big data platform that supports AI and BI products • Work with infra and operations teams to monitor and optimize existing infrastructure • Develop and implement pipelines that extract, transform, and load data into information products that help the organization reach its strategic goals • Help define data governance policies and support data-versioning processes
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