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Barclays

Wealth management services for UK clients

Data Engineer - ETL

Data EngineeringUpdated on Nov 12
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • Experience designing, developing, and maintaining scalable data pipelines using PySpark.
  • Experience building and optimizing ETL/ELT solutions for large-scale enterprise data processing.
  • Ability to develop reusable frameworks, components, and standards for data onboarding and analytics delivery.
  • Experience implementing data quality controls, validation frameworks, and reconciliation processes.
  • Ability to deliver high-quality code following engineering best practices and coding standards, with automated testing approaches.
  • Strong expertise in PySpark, AWS cloud technologies, data engineering practices, and modern data platform architectures.
  • Programming skills in Python and PySpark.
  • Hands-on experience with Databricks and/or Snowflake.
  • Experience with AWS Cloud services including S3, Glue, EMR, Lambda, EC2, DynamoDB, IAM, CloudWatch, and CloudTrail.
  • Hands-on experience developing and optimizing AWS Glue ETL jobs using PySpark.
  • Experience with large-scale distributed data processing.
  • Understanding of Data Lake, Lakehouse, and Modern Data Platform architectures.
  • Expertise with Git-based source control platforms.
  • Experience with Airflow.

Responsibilities

  • Design, develop, and deliver scalable data solutions.
  • Build robust data pipelines, enable data processing, and support large-scale data transformation initiatives across the organization.
  • Collaborate with business stakeholders, architects, product owners, and engineering teams to deliver secure, reliable, and performant data products.
  • Support the successful delivery of location strategy projects to plan, budget, and agreed quality and governance standards.
  • Build and maintain systems that collect, store, process, and analyze data, including data pipelines, data warehouses, and data lakes, ensuring data is accurate, accessible, and secure.
  • Build and maintain data architecture pipelines that enable the transfer and processing of durable, complete, and consistent data.
  • Design and implement data warehouses and data lakes that manage appropriate data volumes and velocity and meet security measures.
  • Develop processing and analysis algorithms suited to the intended data complexity and volumes.
  • Collaborate with data scientists to build and deploy machine learning models.
  • Apply risk and control, change and transformation, business acumen, strategic thinking, and digital and technology skills in the role.

Desired Qualifications

  • Hands-on experience with Databricks and/or Snowflake.
  • Strong experience with AWS Cloud services including S3, Glue, EMR, Lambda, EC2, DynamoDB, IAM, CloudWatch, and CloudTrail.
  • Hands-on experience developing and optimizing AWS Glue ETL jobs using PySpark.
  • Experience with large-scale distributed data processing.
  • Understanding of Data Lake, Lakehouse, and Modern Data Platform architectures.
  • Expertise with Git-based source control platforms.
  • Experience with Airflow.

About the Company

Barclays

Wealth management services for UK clients

Barclays Wealth Management helps UK clients manage and grow their wealth through a regional network of financial experts. It delivers personalized advice and services—such as investment management, financial planning, and estate and trust planning—tailored to each client’s goals, risk tolerance, and time horizon, with support from Barclays’ broader resources. What sets it apart is the combination of local, face-to-face guidance with the scale and range of a large UK bank, offering access to a wide array of financial products and research. The goal is to help clients preserve and grow their wealth over the long term by providing comprehensive, advisor-led guidance aligned with individual financial objectives.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1690

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