Intern in Data Analysis and Data Engineering at a startup in Köln, focusing on software engineering and data analytics. Participate in the launch of an interactive sports app.
About the role
- Data Engineer at Vistra designing and maintaining data pipelines for analytics. Collaborating with teams and optimizing data integration using modern cloud technologies.
Responsibilities
- Design and implement scalable ETL/ELT pipelines using AWS services including AWS Glue, Lambda, S3, and Step Functions
- Build and optimize data integration processes connecting MySQL databases, APIs, and external data sources to analytical systems and data warehouses
- Develop automated data quality monitoring, validation, and cleansing processes
- Create and maintain data models, schemas, and documentation to support analytics teams
- Implement real-time and batch data processing solutions using serverless architectures
- Collaborate with development teams to integrate data collection points into Next.js applications and Node.js services
- Build and maintain data analytics APIs and services
- Monitor data pipeline performance, troubleshoot issues, and implement proactive alerting and logging mechanisms
- Design and implement data backup, archival, and disaster recovery strategies
- Work with data analysts and business stakeholders to understand reporting requirements
Requirements
- Bachelor’s degree in Computer Science, Data Engineering, Mathematics, or a related technical field
- 4-6 years of hands-on data engineering experience with strong proficiency in Python for data processing, transformation, and pipeline development
- Extensive experience with AWS data services including AWS Glue, Lambda, S3, Athena, Redshift, and Kinesis for building serverless data pipelines
- Strong SQL skills and experience with MySQL database design, optimization, and administration including performance tuning and query optimization
- Experience with data pipeline orchestration tools such as Apache Airflow, AWS Step Functions, or similar workflow management systems
- Proficiency in data formats including JSON, CSV, Parquet, and Avro
- Knowledge of data warehousing concepts, dimensional modeling, and analytics best practices for supporting business intelligence requirements
- Experience with version control systems, CI/CD pipelines, and infrastructure as code practices for deploying and managing data infrastructure
- AWS certifications such as AWS Certified Data Analytics Specialty or AWS Certified Solutions Architect
- Experience with streaming data technologies including Apache Kafka, AWS Kinesis, or real-time data processing frameworks
- Knowledge of machine learning workflows and experience building data pipelines that support ML model training and inference
- Familiarity with business intelligence tools such as Tableau, Power BI, or AWS QuickSight for creating data visualizations and dashboards
- Experience with containerization technologies like Docker and orchestration platforms for deploying data processing applications
- Understanding of data governance, privacy regulations, and security best practices for handling sensitive data in cloud environments
- Experience with NoSQL databases such as DynamoDB, MongoDB, or Elasticsearch for handling unstructured data and high-volume analytics workloads
Benefits
- Flexible hybrid working arrangement
- Birthday leave
- Comprehensive medical insurance and dental coverage
- Wellness allowance
- Competitive annual leave entitlement
- Internal mentorship program
- Reimburse professional membership fees for certifications
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
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