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AWS Data Engineer

TerraGiG
Manchester
5 days ago
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Role

AWS Data Engineer

Experience

8+ years

Location

Remote

Time Zone

UK

Duration

2 months (Extendable)


Job Description

  • Design, development, and implementation of performant ETL pipelines using python API (pySpark) of Apache Spark on AWS EMR.
  • Writing reusable, testable, and efficient code
  • Integration of data storage solutions in spark – especially with AWS S3 object storage. Performance tuning of pySpark scripts.
  • Need to ensure overall build delivery quality is good and on-time delivery is done at all times.
  • Should be able to handle meetings with customers with ease.
  • Need to have excellent communication skills to interact with the customer.
  • Be a team player and willing to work in an onsite-offshore model, mentor other folks in the team (onsite as well as offshore)

*5+ years of experience in programming with python. Strong proficiency in python

*Familiarity with functional programming concepts

*3+ years of hands-on experience in developing ETL data pipelines using pySpark on AWS EMR

*Experience in building pipelines and data lake for large enterprises on AWS

*Good understanding of Spark’s Dataframe and API

*Experience in configuring EMR clusters on AWS

*Experience in dealing with AWS S3 object storage from Spark.

*Experience in troubleshooting spark jobs. Knowledge of monitoring spark jobs using Spark UI

*Performance tuning of Spark jobs.

*Understanding fundamental design principles behind business processes

Process Knowledge and Expertise:

  • Demonstrated experience in change management processes, including understanding of governance frameworks and preparation of supporting artefacts required for approvals.
  • Strong clarity on the path to production, with hands-on involvement in deployments, testing cycles, and obtaining business sign-offs.
  • Proven track record in technical solution design, with the ability to provide architectural guidance and support implementation strategies.

Databricks-Specific Skills:

  • Experience in at least developing and delivering end-to-end Proof of Concept (POC) solutions covering the below:
  • Basic proficiency in Databricks, including creating jobs and configuring clusters.
  • Exposure to connecting external data sources (e.g., Amazon S3) to Databricks.
  • Understanding of Unity Catalog and its role in data governance.
  • Familiarity with notebook orchestration and implementing modular code structures to enhance scalability and maintainability.

Important Pointers:

  • Candidates must have actual hands-on work experience, not just home projects or academic exercises.
  • Profiles should clearly state how much experience they have in each skill area, as this helps streamline the interview process.
  • Candidates must know their CV/profile inside out, including all projects and responsibilities listed. Any ambiguity or lack of clarity on the candidate’s part can lead to immediate rejection, as we value accuracy and ownership.
  • They should be able to confidently explain their past experience, challenges handled, and technical contributions.

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