Position: AWS Data Engineer
Company: Emergn
Experience required: 3y
English level: intermediate

**Improve the way people and companies work. Forever.**
 
We are Emergn. We are a different type of services company. We are passionate about helping companies build better products.
 
Our work is complex. Helping our customers solve their hardest problems, in order that they can build better products, is both stimulating and challenging. To serve our clients well, we are building a diverse and talented, global team, who focuses on supporting their colleagues, delighting our clients, and upholding the Emergn Way with pride. In return, we are focused on providing our people with interesting and meaningful work, in an environment where they feel respected, appreciated, and rewarded for their contributions.
 
We are currently seeking a **Data Engineer** to join the team on an exciting and challenging client engagement.

**What we are looking for:**
• Knowledge and expertise with design, implementation, and maintenance of data ingestion pipelines using such AWS technologies as EC2/S3/RDS/Lambda, Glue/GlueCrawler/Athena, JSON/Avro. 
• Experience with stream processing implementation based on Apache Kafka, AWS Kinesis and Kubernetes, and Python queuing experience. 
• Knowledge and expertise with data modeling skills using RabbitMQ.
• Proven ability to build and support large-scale data solutions and high availability stacks.
• Knowledge of software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations.
• Solid written and verbal communication skills.
• Ability to work in a team environment that promotes collaboration.

**What you will be doing:**
• Collaborate with business, engineers, and product managers to understand data needs.
• Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using cloud-native data engineering principles
• Design, build, and scale data pipelines across a variety of source systems and streams (internal, third-party, as well as cloud-based), distributed/elastic environments, and downstream applications and/or self-service solutions
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Implement the appropriate design patterns while optimizing performance, cost, security, and scale, and end-user experience.
• Participate in development sprints, demos, and retrospectives, as well as release and deployment.