Data Engineer
rippling
Job Description
What you'll do
- Collaborate with Colleagues: Work closely with colleagues to understand customers' data requirements and challenges, contributing to the development of robust data solutions tailored to client needs.
- Apply DataOps Principles: Embrace a DataOps mindset and utilize modern data engineering tools and frameworks like Apache Airflow, Apache Spark, or similar, to create scalable and efficient data pipelines and architectures.
- Support Data Engineering Projects: Assist in managing and executing data engineering projects, providing technical support and contributing to project success.
- Promote Knowledge Sharing: Contribute to our knowledge base through technical blogs and articles, advocating for best practices in data engineering, and fostering a culture of continuous learning and innovation.
We're looking for:
- 2+ years of experience in data engineering, data architecture, or related fields, bringing valuable expertise in managing and optimizing data pipelines and architectures.
- Solid track record of contributing to complex data engineering projects, including assisting in the design and implementation of scalable data solutions.
- Hands-on experience with ETL processes, data warehousing, and data modelling tools, enabling the support and delivery of efficient and robust data pipelines.
- Good understanding of data integration tools and best practices, facilitating seamless data flow across systems.
- Familiarity with cloud-based data services and technologies (e.g., AWS Redshift, Azure Synapse Analytics, Google BigQuery) ensuring effective utilization of cloud resources for data processing and analytics.
- Strong analytical skills to address data challenges and support data-driven decision-making.
- Proficiency in implementing and optimizing data pipelines using modern tools and frameworks.
- Strong communication and interpersonal skills enabling effective collaboration with cross-functional teams and stakeholder engagement.