Data Engineer

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 organization's 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.