AWS Data Engineer

Job Description

Purpose of the role

To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure. 

Accountabilities

• Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
• Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
• Development of processing and analysis algorithms fit for the intended data complexity and volumes.
• Collaboration with data scientist to build and deploy machine learning models.

Analyst Expectations

• To perform prescribed activities in a timely manner and to a high standard consistently driving continuous improvement.
• Requires in-depth technical knowledge and experience in their assigned area of expertise
• Thorough understanding of the underlying principles and concepts within the area of expertise
• They lead and supervise a team, guiding and supporting professional development, allocating work requirements and coordinating team resources.
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.
• OR for an individual contributor, they develop technical expertise in work area, acting as an advisor where appropriate.
• Will have an impact on the work of related teams within the area.
• Partner with other functions and business areas.
• Takes responsibility for end results of a team’s operational processing and activities.
• Escalate breaches of policies / procedure appropriately.
• Take responsibility for embedding new policies/ procedures adopted due to risk mitigation.
• Advise and influence decision making within own area of expertise.
• Take ownership for managing risk and strengthening controls in relation to the work you own or contribute to. Deliver your work and areas of responsibility in line with relevant rules, regulation and codes of conduct.
• Maintain and continually build an understanding of how own sub-function integrates with function, alongside knowledge of the organisations products, services and processes within the function.
• Demonstrate understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
• Make evaluative judgements based on the analysis of factual information, paying attention to detail.
• Resolve problems by identifying and selecting solutions through the application of acquired technical experience and will be guided by precedents.
• Guide and persuade team members and communicate complex / sensitive information.
• Act as contact point for stakeholders outside of the immediate function, while building a network of contacts outside team and external to the organisation.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.

Join us as an AWS Data Engineer Barclays, responsible for supporting the successful delivery of Location Strategy projects to plan, budget, agreed quality and governance standards. You'll spearhead the evolution of our digital landscape, driving innovation and excellence. You will harness cutting-edge technology to revolutionise our digital offerings, ensuring unparalleled customer experiences.

To be successful as an AWS Data Engineer you should have experience with: -

• 5+ years of experience in developing, testing and maintaining applications on AWS Cloud.

• Strong hands‑on experience with the AWS Data Analytics stack (Amazon S3, AWS Glue, Athena, Lambda, IAM, Lake Formation, KMS, STS, and Step Functions), with a proven ability to build, test, and support secure, scalable, and well‑governed data pipelines.

• Firsthand experience in Airflow and PySpark and strong knowledge of Python.

• Design and implement scalable and efficient data transformation/storage solutions using Snowflake.

• Experience in Data ingestion to Snowflake for different storage format such Parquet, Iceberg, JSON, CSV etc.

• Experience in using DBT (Data Build Tool) with snowflake for ELT pipeline development.

• Experience in Writing advanced SQL and PL SQL programs.

• Experience in AWS data pipeline development.

• HandsOn Experience for building reusable components using Snowflake and AWS Tools/Technology.

• Must have completed two major projects.

• Exposure to data governance or lineage tools such as Immuta and Alation is added advantage.

• Experience in using Orchestration tools such as Apache Airflow or Snowflake Tasks is added advantage.

• Knowledge on Ab Initio ETL tool is a plus.

• Hands on experience in Unix scripting is a plus.

• Automation based on tools like selenium, java is a plus.

Some other highly valued skills may include: -

• Engage stakeholders, gather requirements/user stories, and translate them into ETL components.

• Understand infrastructure setup and provide solutions independently or in collaboration with teams.

• Good knowledge of Data Marts and Data Warehousing concepts.

• Excellent analytical and people skills.

• Implement a cloud-based enterprise data warehouse using multiple platforms, including Snowflake and NoSQL, to develop data movement strategies.

You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills.

This role is based in Pune and Chennai.