Qode via Workable

AWS Data Engineer

Level not stated United States

still open verified 9h ago posted 228d ago checked just now

Apply at apply.workable.com

This is the employer's own posting, not a copy on a job board.

What we know

Is it still open?

Confirmed still open

Last checked 9h ago — checked against the employer's own applicant tracking system, which is the company answering directly.

We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.

Check this listing's status as JSON

How old is it?

Posted 228d ago

The date the source published, not the day we noticed it (2026-01-30). Last seen at its source just now.

Is it remote?

Marked remote on the employer's board

Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.

Who may apply?

United States

The description states no restriction of its own. This is the source's own tag.

Pay not stated

Similar roles pay $160k–225k/yr

Middle 50% of 2159 listings that do state pay — Engineering · all levels · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.

Skills named in the ad

AWSCI/CDDatabricksETLElectronic Health RecordsPower BIPythonSnowflakeTableau

Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.

Carried by 1 source

workable employer's own board first seen 11d ago · last seen just now

The listing

Job Brief

As an AWS Data Engineer, your role will be to design, develop, and maintain scalable data pipelines on AWS. You will work closely with technical analysts, client stakeholders, data scientists, and other team members to ensure data quality and integrity while optimizing data storage solutions for performance and cost-efficiency. This role requires leveraging AWS native technologies and Databricks for data transformations and scalable data processing.

Responsibilities

• Lead and support the delivery of data platform modernization projects.

• Design and develop robust and scalable data pipelines leveraging AWS native services.

• Optimize ETL processes, ensuring efficient data transformation.

• Migrate workflows from on-premise to AWS cloud, ensuring data quality and consistency.

• Design automations and integrations to resolve data inconsistencies and quality issues

• Perform system testing and validation to ensure successful integration and functionality.

• Implement security and compliance controls in the cloud environment.

• Ensure data quality pre- and post-migration through validation checks and addressing issues regarding completeness, consistency, and accuracy of data sets.

• Collaborate with data architects and lead developers to identify and document manual data movement workflows and design automation strategies.

Skills and Requirements

· 10+ years’ experience with a core data engineering skillset leveraging AWS native technologies (AWS Glue, Python, Snowflake, S3, Redshift).

· Experience in the design and development of robust and scalable data pipelines leveraging AWS native services.

· Proficiency in leveraging Snowflake for data transformations, optimization of ETL pipelines, and scalable data processing.

· Experience with streaming and batch data pipeline/engineering architectures.

· Familiarity with DataOps concepts and tooling for source control and setting up CI/CD pipelines on AWS.

· Hands-on experience with Databricks and a willingness to grow capabilities.

· Experience with data engineering and storage solutions (AWS Glue, EMR, Lambda, Redshift, S3).

· Strong problem-solving and analytical skills.

· Knowledge of Dataiku is needed

· Graduate/Post-Graduate degree in Computer Science or a related field.

· AWS S3 (data storage, export, recall)

· Athena (querying data lakes)

· Data pipelines (batch & near-real-time)

· Integration with external systems (FHIR)

· Secure data handling (KMS, Macie)

· Cloud-native analytics

· Multi-account, multi-region data architecture

· BI integrations: Power BI, Tableau, QuickSight