Senior Data Engineer

November 27, 2024

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Rearc

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Rearc is a boutique, engineering-driven services firm specializing in cloud, data, and generative AI solutions. They are known for their innovation and engineering delivery excellence, empowering small teams to achieve significant results and foster creativity. Rearc offers services such as GenAI quickstart, secure data collaboration, data platform foundation, and cloud platform engineering. Their services are comprehensive and adaptable to meet the specific needs of their clients. Rearc has expertise in building foundational data platform capabilities, cloud governance, and the use of AWS products.

Cloud Computing • DevOps • Software Development • IT Transformation

51 - 200 employees

🤖 Artificial Intelligence

📋 Description

• Provide strategic vision and technical leadership in data engineering, guiding the development and execution of advanced data strategies that align with business objectives. • Design and architect complex data pipelines and scalable architectures, leveraging advanced tools and frameworks (e.g., Apache Kafka, Kubernetes) to ensure optimal performance and reliability. • Lead the exploration and adoption of new technologies and methodologies in data engineering, driving innovation and continuous improvement across data processes. • Apply deep expertise in ETL processes, data modelling, and data warehousing to optimize data workflows and ensure data integrity and quality. • Collaborate closely with cross-functional teams to understand requirements and deliver impactful data solutions—mentor and coach junior team members, fostering their growth and development in data engineering practices. • Contribute to thought leadership in the data engineering domain through technical articles, conference presentations, and participation in industry forums.

🎯 Requirements

• 8+ years of experience in data engineering, showcasing expertise in diverse architectures, technology stacks, and use cases. • Strong expertise in designing and implementing data warehouse and data lake architectures, particularly in AWS environments. • Proven experience with data pipeline orchestration using platforms such as Airflow, Prefect, or AWS Glue. • Hands-on experience with data analysis tools and libraries like NumPy, Pandas, or Dask. • Proficiency with Spark and AWS EMR is highly desirable. • Experience with SQL and NoSQL databases, including PostgreSQL, Amazon Redshift, Delta Lake, and DynamoDB. • In-depth knowledge of data architecture principles and best practices, especially in cloud environments. • Proven experience with AWS services, including expertise in using AWS CLI, SDK, and Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or AWS CDK. • Exceptional communication skills, capable of clearly articulating complex technical concepts to both technical and non-technical stakeholders. • Demonstrated ability to quickly adapt to new tasks and roles in a dynamic environment.