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- Tasks: Join us as a Data Engineer to design and maintain our data infrastructure.
- Company: Iceberg is at the forefront of data-driven decision-making.
- Benefits: Enjoy flexible work options, competitive pay, and a collaborative culture.
- Why this job: Be part of innovative projects that empower data initiatives and enhance your skills.
- Qualifications: Bachelor's or master's in computer science with 5-8 years of data engineering experience required.
- Other info: Experience with Iceberg file formats and AWS services is a must!
The predicted salary is between 43200 - 72000 £ per year.
Job Description:
As a Data Engineer with Iceberg experience, you will play a crucial role in the design, development, and maintenance of our data infrastructure. Your work will empower data-driven decision-making and contribute to the success of our data-driven initiatives.
Key Responsibilities:
- Data Integration: Develop and maintain data pipelines to extract, transform, and load (ETL) data from various sources into AWS data stores for both batch and streaming data ingestion.
- AWS Expertise: Utilize your expertise in AWS services such as Amazon EMR , S3, AWS Glue, Amazon Redshift, AWS Lambda, and more to build and optimize data solutions.
- Data Modeling: Design and implement data models to support analytical and reporting needs, ensuring data accuracy and performance.
- Data Quality: Implement data quality and data governance best practices to maintain data integrity.
- Performance Optimization: Identify and resolve performance bottlenecks in data pipelines and storage solutions to ensure optimal performance.
- Documentation: Create and maintain comprehensive documentation for data pipelines, architecture, and best practices.
- Collaboration: Collaborate with cross-functional teams, including data scientists and analysts, to understand data requirements and deliver high-quality data solutions.
- Automation: Implement automation processes and best practices to streamline data workflows and reduce manual interventions.
- Experience working with bigdata ACID file formats to build delta lake, particularly with Iceberg file formats and loading methods of Iceberg.
- Good knowledge on Iceberg functionalities to use the delta features to identify the changed records, optimization, and housekeeping on Iceberg tables in the data lake.
Must have: AWS, ETL, EMR, GLUE, Spark/Scala, Java, Python,
Good to have: Cloudera – Spark, Hive, Impala, HDFS , Informatica PowerCenter, Informatica DQ/DG, Snowflake Erwin
Qualifications:
- Bachelor\’s or master’s degree in computer science, Data Engineering, or a related field.
- 5 to 8 years of experience in data engineering, including working with AWS services.
- Proficiency in AWS services like S3, Glue, Redshift, Lambda, and EMR.
- Knowledge on Cloudera based hadoop is a plus.
- Strong ETL development skills and experience with data integration tools.
- Knowledge of data modeling, data warehousing, and data transformation techniques.
- Familiarity with data quality and data governance principles.
- Strong problem-solving and troubleshooting skills.
- Excellent communication and teamwork skills, with the ability to collaborate with technical and non-technical stakeholders.
- Knowledge of best practices in data engineering, scalability, and performance optimization.
Experience with version control systems and DevOps practices is a plus.
Infinity Quest | Data Engineer employer: Infinity Quest
Contact Detail:
Infinity Quest Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Infinity Quest | Data Engineer
✨Tip Number 1
Familiarize yourself with Iceberg file formats and their functionalities. Understanding how to utilize delta features for identifying changed records will set you apart from other candidates.
✨Tip Number 2
Showcase your AWS expertise by discussing specific projects where you've used services like S3, Glue, and Redshift. Highlighting real-world applications of these tools can demonstrate your hands-on experience.
✨Tip Number 3
Prepare to discuss your approach to data quality and governance. Be ready to share examples of how you've implemented best practices in previous roles to maintain data integrity.
✨Tip Number 4
Emphasize your collaboration skills by providing examples of how you've worked with cross-functional teams. Being able to communicate effectively with both technical and non-technical stakeholders is crucial for this role.
We think you need these skills to ace Infinity Quest | Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure to customize your CV to highlight your experience with AWS services, ETL processes, and data engineering. Emphasize your proficiency in Iceberg file formats and any relevant projects you've worked on.
Craft a Strong Cover Letter: In your cover letter, express your passion for data engineering and how your skills align with the responsibilities outlined in the job description. Mention specific experiences that demonstrate your expertise in AWS and data pipeline development.
Showcase Relevant Projects: If you have worked on projects involving data integration, automation, or performance optimization, be sure to include these in your application. Provide details about the technologies used and the impact of your work.
Highlight Collaboration Skills: Since collaboration is key in this role, mention any experience you have working with cross-functional teams. Highlight your communication skills and ability to work with both technical and non-technical stakeholders.
How to prepare for a job interview at Infinity Quest
✨Showcase Your AWS Expertise
Make sure to highlight your experience with AWS services like S3, Glue, Redshift, and EMR. Be prepared to discuss specific projects where you utilized these tools to solve data engineering challenges.
✨Demonstrate Your ETL Skills
Prepare to explain your approach to developing and maintaining ETL pipelines. Discuss any challenges you've faced in data integration and how you overcame them, especially in relation to batch and streaming data ingestion.
✨Discuss Data Quality Practices
Be ready to talk about your experience with data quality and governance. Share examples of how you've implemented best practices to maintain data integrity and ensure accurate reporting.
✨Collaboration is Key
Emphasize your teamwork skills by sharing experiences where you collaborated with data scientists or analysts. Highlight how you gathered requirements and delivered high-quality data solutions that met their needs.
