Customer Support Service Manager

Data Engineer A Data Engineer typically requires 36 years of experience in designing, building, and optimizing big data pipelines, architectures, and data sets. Key experience includes proficiency in SQL, Python/Scala/Java, ETL processes, and cloud platforms (AWS, Azure, or GCP). Candidates should possess strong skills in data modeling, warehousing (Snowflake, Redshift), and distributed systems like Spark or Kafka. Core Experience & Qualifications Technical Proficiency: 3-5+ years of experience in data engineering, data warehousing, or software engineering roles. Data Pipelines (ETL/ELT): Extensive experience in designing, building, and maintaining robust, scalable data pipelines. Languages: Strong programming skills in Python, Java, or Scala for data processing and automation. SQL & Database Management: Expert-level SQL skills for querying and manipulating data in relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) databases. Big Data Technologies: Hands-on experience with Apache Spark, Hadoop, Kafka, or similar frameworks for large-scale data processing. Cloud Platforms: Experience with cloud services such as AWS (S3, EMR, Redshift), Azure (Data Factory, Data Lake), or GCP. Key Responsibilities & Competencies Data Modeling & Warehousing: Designing efficient, secure data models (star/snowflake schemas). Data Infrastructure: Managing data storage systems and ensuring data quality and security. Collaboration: Working with data scientists and analysts to support data-driven initiatives. Tools: Familiarity with workflow orchestration tools like Apache Airflow. Education & Education Background Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related quantitative field. Preferred Skills Experience with containerization tools (Docker, Kubernetes). Knowledge of data visualization tools (Tableau, Power BI). Certifications in cloud platforms (AWS, GCP) or big data technologies.