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Chief Data Pipeline Architect
2 weeks ago
To succeed as a Cloud Data Engineer, you will need to design and implement scalable data pipelines that integrate with various systems.
Responsibilities:
- Develop cloud-based data architectures using big data formats like Apache Parquet and ORC.
- Implement robust data models, including relational, dimensional, and NoSQL models.
- Collaborate with cross-functional teams to gather and refine requirements and deliver high-quality solutions.
- Deploy infrastructure using Infrastructure as Code (IaC) tools such as AWS CloudFormation or CDK.
- Monitor and orchestrate workflows using Apache Airflow or Dagster.
- Follow best practices in data governance, quality, and security.
Requirements:
- Experience: At least 3 years in a data engineering role working on data integration, processing, and transformation use cases with open-source languages and cloud technologies.
- Strong programming skills in Python specifically for API integration and data libraries, with emphasis on quality and test-driven development.
- Demonstrated proficiency with big data storage formats and practical knowledge of pitfalls and optimization strategies.
- Demonstrated proficiency with SQL
- Experience with data modeling: Relational modeling, Dimensional modeling, NoSQL modeling.
- Working knowledge of IaC on AWS: CloudFormation or CDK.
- Working knowledge of AWS Services: Glue, IAM, Lambda, DynamoDb, Step Functions, S3, CloudFormation or CDK.
- Experience with orchestration of data flows/pipelines: Apache Airflow or Dagster.
Key Competencies & Abilities:
- Ability to work independently and collaboratively in a team environment.
- High level of attention to detail and commitment to delivering quality work.
- Strong analytical and critical thinking skills.
- Effective time management and organizational skills.
- Strong customer focus and the ability to communicate technical concepts clearly to stakeholders.
- Excellent written and verbal communication skills.