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Data Engineer

JR United Kingdom

Woking

Remote

GBP 50,000 - 70,000

Full time

30+ days ago

Job summary

A UK-based company is seeking a Data Engineer to design and implement robust data pipelines that ensure the integrity and reliability of data. Candidates should have strong proficiency in PySpark, AWS, and ETL development, with a focus on driving innovation in data management. This role offers opportunities for hands-on experience with the latest cloud technologies and requires a proactive attitude towards data-driven solutions.

Qualifications

  • Strong command of PySpark for data processing and AWS for cloud-based solutions.
  • Experience in designing, implementing, and debugging ETL pipelines.
  • Solid understanding of Python, PySpark, and SQL for data manipulation.
  • In-depth knowledge of Apache Spark and Apache Airflow.
  • Experience in designing data pipelines using AWS cloud-native services.
  • Extensive knowledge of AWS services like S3, RDS, Redshift, and Lambda.
  • Proficient in deploying AWS resources using Terraform.
  • Hands-on experience with CI/CD workflows using GitHub Actions.

Responsibilities

  • Design and implement robust data pipelines for efficient data flow.
  • Ensure the integrity, reliability, and accessibility of data.
  • Support critical business decisions and drive insights.

Skills

Proficiency in PySpark and AWS
ETL Pipeline Development
Programming Expertise in Python
Knowledge of Spark and Airflow
Cloud-Native Services
AWS Services
Terraform for Deployment
CI/CD Workflows
Job description

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London Remote

**About the Role**

The Data Engineer will play a pivotal role in organization by designing and implementing robust data pipelines that facilitate efficient data flow and management across various platforms. This position is essential for ensuring the integrity, reliability, and accessibility of our data, which supports critical business decisions and drives insights.

**Required Skills**

- **Proficiency in PySpark and AWS:** You should have a strong command of both PySpark for data processing and AWS (Amazon Web Services) for cloud-based solutions.

- **ETL Pipeline Development:** Demonstrated experience in designing, implementing, and debugging ETL (Extract, Transform, Load) pipelines is crucial. You will be responsible for moving and transforming data from various sources to data warehouses.

- **Programming Expertise:** A solid understanding of Python, PySpark, and SQL is required to manipulate and analyze data efficiently.

- **Knowledge of Spark and Airflow:** In-depth knowledge of Apache Spark for big data processing and Apache Airflow for orchestrating complex workflows is essential for managing data pipelines.

- **Cloud-Native Services:** Experience in designing data pipelines leveraging cloud-native services on AWS to ensure scalability and reliability in data handling.

- **AWS Services:** Extensive knowledge of various AWS services, such as S3, RDS, Redshift, and Lambda, will be necessary for building and managing our data infrastructure.

- **Terraform for Deployment:** Proficient in deploying AWS resources using Terraform, ensuring that infrastructure as code is implemented effectively.

- **CI/CD Workflows:** Hands-on experience in setting up Continuous Integration and Continuous Deployment (CI/CD) workflows using GitHub Actions to automate the deployment process and enhance collaboration within the team.

**Preferred Skills**

- **Experience with Other Cloud Platforms:** Familiarity with additional cloud platforms, such as Google Cloud Platform (GCP) or Microsoft Azure, will be advantageous and broaden your impact within our data architecture.

- **Data Governance and Compliance:** Understanding of data governance frameworks and compliance standards will be beneficial as we prioritize data privacy and regulatory requirements.

We are looking for a proactive and detail-oriented Data Engineer who is passionate about working with data and driving innovation . If you have a strong technical background and a commitment to excellence, we would love to hear from you!

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