At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

Job Description The Data Engineering Head provides management and technical guidance, as well as overall strategy to the Data Engineering Team. The Head drives the delivery and implementation of automated data pipelines within a complex and multi-platform environment. He will also help and mentor the team of developers as well as guide the team by providing overall engineering strategy. He will help ensure compliance with data engineering best practices and data governance standards across the enterprise.

DUTIES AND RESPONSIBILITIES:Data Engineering Strategy & Execution:  formulate strategy including (but not limited to) tools, framework and architecture and Act as point of contact for senior management for solutioning and delivery of key data engineering initiativesDefines, prioritizes and allocates resources; tracks and provides status reports of work assignments, projects, and programs performed in support of the Data Engineering team. Review business and technical requirements and ensure the data integration platform meets requirementsData Pipeline Development: Leading the design and implementation of robust data pipelines for ingesting, processing, and transforming data from various sources into usable formats for analysis, applying best practices for for automated data pipeline design and developmentLead design, development, and implementation of end-to-end complex automated data pipeline  system using common Big Data Tools such as (but not limited to) Apache Spark, Talend, Kafka, Airflow, NiFi i and Hadoop ToolsPerformance Optimization: Identifying bottlenecks and optimizing data processing and storage systems for improved performance and efficiency Implement monitoring and measurement of data quality as defined by business rulesTechnology Evaluation and Adoption: Staying abreast of emerging technologies and trends in data engineering, evaluating their potential impact and suitability for the organization, and overseeing their adoption where appropriate.Team Leadership and Development: Building and leading a team of data engineers, providing mentorship, guidance, and support to enable their professional growth and foster a collaborative and innovative culture.Cross-functional Collaboration: Collaborating with other departments such as data science, analytics, and IT to understand their data needs and requirements, and aligning data engineering efforts to support organizational goals and initiatives.Compliance and SecurityEnsuring compliance with data privacy regulations and implementing robust security measures to protect sensitive data from unauthorized access or breaches.REQUIREMENTS:8-10 years experience in people management, 5-8 years Java Experience8 years of experience in ETL development and managementExposure to company leadership/management and technical solutioning for key business requirementsIn-depth experience in automated data pipeline design, implementation, and support. Experience with Big Data Platforms.Experience establishing and building operational data stores and analytic data stores (enterprise data warehouse)Strong SQL & PL/SQL skills with the ability to solve highly complex challengesAdvanced technical experience and business knowledge in various SDLC methodologies including waterfall, iterative, agile software development life cycle or related disciplines/processes is preferred.Experience in DevSecOps ProcessBachelor’s degree in Computer Science or  an information technology related  fieldsSkills:In-depth knowledge of Tier 1 Data Engineering tools is preferred, e.g. Talend, Apache Spark, NiFi, Kafka, Airflow.Working knowledge of Apache Spark, Distributed computing and other Hadoop-related conceptsAdvanced technical and business knowledge in software development life cycle, quality assurance, project management, and other related disciplines/processes.Understanding of data engineering design and development best practices.Understanding of data governance, master data management best practices.

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