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# Senior Associate AWS Data Engineer

- **Company:** Publicis Groupe
- **Location:** Los Angeles, CA, United States
- **Experience:** Expert
- **Salary:** $103,000.0 - $154,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Agile Methodology, Amazon Web Services, Amazon S3, Computing Platforms, Big Data, Code Review, Databases, Continuous Integration, Data Validation, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Amazon DynamoDB, Graph Database, Python (Programming Language), Metadata, Metadata Repositories, Microsoft SQL Server, MySQL, NoSQL, Oracle (Applications), Cloud Services, SQL Databases, Data Streaming, Systems Integration, Software Repository, Data Processing, Google Cloud, Cloud Platform System, Apache Spark, Cloudformation, Event Driven Architecture, Data Lakes, Pyspark, Information Technology, Data Lineage, AWS Glue, AWS Data Analytics, Cosmos DB, Data Management, Vertica, Functional Programming, Terraform, Data Pipelines, Azure Resource Manager, Databricks
- **Published:** May 19, 2026
- **Apply:** https://www.juju.com/job/00000000g0vsv5

## About the Role

+ Demonstrable experience in data platforms involving implementation of end to end data pipelines

 + Hands-on experience with Amazon Web Services Cloud Platform

 + Implementation experience with column-oriented database technologies (i.e., Redshift, Vertica), NoSQL database technologies (i.e., DynamoDB, Cosmos DB, etc.) and traditional database systems (i.e., SQL Server, Oracle, MySQL)

 + Experience in implementing data pipelines for both streaming and batch integrations using tools/frameworks like Glue ETL, Lambda, Spark, pysparkStreaming, etc.

 + Ability to handle module or track level responsibilities and contributing to tasks "hands-on"

 + Experience in data modeling, warehouse design and fact/dimension implementations

 + Experience working with code repositories and continuous integration

 + Data modeling, querying, and optimization for relational, NoSQL, timeseries, and graph databases and data warehouses and data lakes

 + Data processing programming using SQL, DBT, Python.

 + Experience with data processing platforms such as Databricks

 + Logical programming in Python, Spark, PySpark, Java, Javascript, and/or Scala

 + Data ingest, validation, and enrichment pipeline design and implementation

 + Cloud-native data platform design with a focus on streaming and event-driven architectures

 + Test programming using automated testing frameworks, data validation and quality frameworks, and data lineage frameworks

 + Metadata definition and management via data catalogs, service catalogs, and stewardship tools such as AWS Glue Catalog, OpenMetadata, DataHub, Alation, and similar

 + Code review and mentorship

 + Bachelor's degree in Computer Science, Engineering or related field.

## Description

We are looking for a Senior Associate Data Engineer role focused on building and supporting scalable data pipelines on AWS. You'll work hands-on with PySpark, SQL, Python, Databricks, and AWS services such as Redshift, EMR, Glue, and S3, partnering with onshore and offshore teams in a client-facing environment.

Hybrid role based in Los Angeles (3 days onsite).

Responsibilities

 + Combine your technical expertise and problem-solving passion to work closely with clients, turning complex ideas into end-to-end solutions that transform our clients' business

 + Lead, design, develop and deliver large-scale data systems, data processing and data transformation projects that delivers business value for clients

 + Automate data platform operations and manage the post-production system and processes

 + Conduct technical feasibility assessments and provide project estimates for the design and development of the solution

 + Provide technical inputs to agile processes, such as epic, story, and task definition to resolve issues and remove barriers throughout the lifecycle of client engagements

 + Creation and maintenance of infrastructure-as-code for cloud, on-prem, and hybrid environments using tools such as Terraform, CloudFormation, Azure Resource Manager, Helm, and Google Cloud Deployment Manager


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