Senior Data Engineer

Job Description

Role Summary As a Data Engineer at GFT, you will play a pivotal role in designing, maintaining, and enhancing various analytical and operational services and infrastructure crucial for the organizations functions. Youll collaborate closely with cross-functional teams to ensure the seamless flow of data for critical decision-making processes Key Activities Data Infrastructure Design and Maintenance: Architect, maintain, and enhance analytical and operational services and infrastructure, including data lakes, databases, data pipelines, and metadata repositories, to ensure accurate and timely delivery of actionable insights Collaboration: Work closely with data science teams to design and implement data schemas and models, integrate new data sources with product teams, and collaborate with other data engineers to implement cutting-edge technologies in the data space Data Processing: Develop and optimize large-scale batch and real-time data processing systems to support the organizations growth and improvement initiatives Workflow Management: Utilize workflow scheduling and monitoring tools like Apache Airflow and AWS Batch to ensure efficient data processing and management Quality Assurance: Implement robust testing strategies to ensure the reliability and usability of data processing systems Continuous Improvement: Stay abreast of emerging technologies and best practices in data engineering, and propose and implement optimizations to enhance development efficiency Required Skills Technical Expertise: Proficient in Unix environments, distributed and cloud computing, Python frameworks (e.g., pandas, pyspark), version control systems (e.g., git), and workflow scheduling tools (e.g., Apache Airflow) Database Proficiency: Experience with columnar and big data databases like Athena, Redshift, Vertica, and Hive/Hadoop Cloud Services: Familiarity with AWS or other cloud services like Glue, EMR, EC2, S3, Lambda, etc. Containerization: Experience with container management and orchestration tools like Docker, ECS, and Kubernetes CI/CD: Knowledge of CI/CD tools such as Jenkins, CircleCI, or AWS CodePipeline Nice-to-have requirements : Programming Languages: Familiarity with JVM languages like Java or Scala Database Technologies: Experience with RDBMS (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., DynamoDB, Redis) BI Tools: Exposure to enterprise BI tools like Tableau, Looker, or PowerBI Data Science Environments: Understanding of data science environments like AWS Sagemaker or Databricks Monitoring and Logging: Knowledge of log ingestion and monitoring tools like ELK stack or Datadog Data Privacy and Security: Understanding of data privacy and security tools and concepts Messaging Systems: Familiarity with distributed messaging and event streaming systems like Kafka or RabbitMQ