Saatchi & Saatchi

Senior Associate Big Data Cloud Engineering - Atlanta Hybrid

Saatchi & Saatchi • $107K — $130K *
Atlanta, GA 30349Hybrid
Information Technology
Less than 5 years of experience
Today
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Job Overview by Ladders

Qualifications

  • 5+ years of experience with end-to-end data pipelines and production-grade data systems.
  • Proficient with at least one major cloud platform (AWS, Azure, or GCP).
  • Strong background in Databricks and related technologies like Delta Lake.
  • Expert in Python for data engineering and automation tasks.
  • Experience with both SQL and NoSQL databases, including BigQuery and DynamoDB.
  • Familiarity with streaming and batch integrations using frameworks like Spark and Glue ETL.
  • Knowledgeable in MLOps principles and the associated data engineering responsibilities.

Responsibilities

  • Lead the design and development of scalable data systems for clients.
  • Transform client needs into actionable data solutions that drive business value.
  • Automate operations of data platforms and monitor their performance post-launch.
  • Build efficient data pipelines across cloud platforms for both batch and streaming.
  • Guide the team in developing high-quality data foundations for AI applications.
  • Mentor junior engineers and contribute to hands-on solution delivery.
  • Conduct feasibility assessments and provide estimates for project solutions.

Benefits

  • Flexible vacation policy without limits or accruals.
  • 15 paid holidays yearly.
  • Generous parental leave and transition program for new parents.
  • Tuition reimbursement support for ongoing education.
  • Corporate gift matching to support charitable contributions.
  • Access to continuous learning and development opportunities.
  • Comprehensive health benefits for employees and families.
  • Wellness programs including employee assistance.
  • Culture promoting diversity and teamwork.
  • Flexibility to enhance work-life balance.
Full Job Description
Overview

This is a hybrid role

Publicis Sapient is looking for a Senior Associate Data Engineer to be part of our team of top-notch technologists. You will lead and deliver technical solutions for large-scale digital transformation projects. Working with the latest data and AI engineering technologies in the industry, you will be instrumental in helping our clients evolve for a more digital and AI-enabled future.

Your Impact:
  • Combine your technical expertise and problem-solving passion to work closely with clients, turning complex ideas into end-to-end data solutions that transform our clients' business.
  • Translate client requirements into system design and develop solutions that deliver measurable business value.
  • Lead, design, develop and deliver large-scale data systems, data processing, data transformation, and data platform modernization initiatives.
  • Build and optimize batch and streaming data pipelines across modern cloud data platforms and distributed processing frameworks.
  • Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions.
  • Automate data platform operations and manage post-production systems, observability, quality, reliability, and operational processes, including telemetry pipelines that capture prompt, response, trace, latency, token, and cost data for AI-enabled services in a query able form.
  • Conduct technical feasibility assessments and provide project estimates for the design and development of solutions.
  • Mentor, support, and grow junior team members while contributing hands-on to delivery.


Qualifications

Your Skills & Experience:
  • Demonstrable experience implementing end-to-end data pipelines and production-grade data platforms.
  • Hands-on experience with at least one leading public cloud data platform: Amazon Web Services, Microsoft Azure, or Google Cloud Platform;
  • Experience with Databricks as a data engineering platform is strongly preferred, including working with notebooks, jobs, Delta Lake, or similar lakehouse patterns.
  • Strong Python proficiency and practical experience using Python-based tooling for data engineering, automation, platform development, or AI engineering workflows.
  • Implementation experience with column-oriented database technologies such as BigQuery, Redshift, Vertica, or similar platforms; NoSQL database technologies such as DynamoDB, Bigtable, Cosmos DB, or similar; and traditional database systems such as SQL Server, Oracle, or MySQL.
  • Experience implementing data pipelines for both streaming and batch integrations using tools and frameworks such as Glue ETL, Lambda, Google Cloud Dataflow, Azure Data Factory, Spark, Spark Streaming, or similar technologies.
  • Experience with data modeling, warehouse design, fact/dimension implementations, and modern lakehouse or data mesh patterns.
  • Experience with code repositories, continuous integration, automated testing, release management, and production support practices.
  • Familiarity with MLOps concepts and the data engineering responsibilities required to support AI/ML deployment, validation, monitoring, rollback, and operational reliability.
  • Ability to handle module or track-level responsibilities while contributing to tasks hands-on.
  • Good communication skills and willingness to work as part of a collaborative, cross-functional team.

AI Engineering & Modern Data Platform Experience:
  • Exposure to AI engineering patterns, including context engineering, retrieval-augmented generation support patterns, agent architectures, and production data services that support AI-enabled experiences.
  • Experience building and maintaining the pipelines behind retrieval systems, including document parsing, chunking, metadata extraction, embedding generation, and incremental reindexing, alongside the vector databases, graph databases, semantic search, and knowledge retrieval structures they feed.
  • Exposure to agentic platforms or cloud AI services such as Vertex AI, Azure AI services, AWS AI services, or comparable platforms; specific platform experience is less important than understanding how AI engineering differs from traditional data engineering.
  • Practical experience deploying agents, integrating agent frameworks, or supporting agentic workflows in production or near-production environments is a plus.
  • Experience building evaluation data infrastructure for AI systems, including ground-truth and golden datasets, offline evaluation pipelines, and the data scaffolding behind LLM-as-judge and regression testing.
  • Experience modeling and persisting agent state, including session context, conversation history, and memory stores, treating them as a durable storage and data modeling problem rather than an application detail.
  • Support AI-enabled engineering use cases by designing high-quality data foundations, retrieval patterns, context engineering approaches, and scalable data services that power agentic and machine learning solutions, applying the same lineage, provenance, and data contract rigor to context and retrieval sources that you would to a production warehouse.
  • Experience with agentic harnesses or orchestration tools such as Pi, Hermes Agent, or similar platforms is a plus, but not required.
  • Experience with Snowflake and zero-copy architecture patterns is a plus, particularly for retail, financial services, energy, or CPG-oriented use cases.

Set Yourself Apart With:
  • Developer certifications for AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related cloud/data platforms.
  • Demonstrated experience applying AI engineering concepts in practical business environments rather than only academic or research settings.
  • Hands-on experience supporting AI/ML and LLM lifecycle needs such as model deployment support, monitoring, validation, shadow deployments, release management, and evaluation or data quality measurement for both predictive models and generative systems.
  • Experience in retail, financial services, energy, CPG, logistics, manufacturing, or other data-rich industries where applied AI and large-scale data engineering are used to solve operational or client-facing problems.
  • Understanding of Agile, product, and delivery methodologies in consulting or client-facing environments.

Additional information

Salary Range: $107,000 - $130,000

The range shown represents a grouping of relevant ranges currently in use at Publicis Sapient. Actual range for this position may differ, depending on location and specific skillset required for the work itself.

Benefits of Working Here:
  • Flexible vacation policy; time is not limited, allocated, or accrued.
  • 15 paid holidays throughout the year.
  • Generous parental leave and new parent transition program.
  • Tuition reimbursement.
  • Corporate gift matching program.
  • An inclusive workplace that promotes diversity and collaboration.
  • Access to ongoing learning and development opportunities.
  • Competitive compensation and benefits package.
  • Flexibility to support work-life balance.
  • Comprehensive health benefits for you and your family.
  • Generous paid leave and holidays.
  • Wellness program and employee assistance.

About Saatchi & Saatchi

Saatchi & Saatchi is a global advertising agency headquartered in New York City. The company was founded in London in 1970 by brothers Maurice and Charles Saatchi and is now part of the Publicis Groupe, a French multinational advertising and public relations company. Saatchi & Saatchi has over 6,000 employees in 114 countries and provides a range of advertising and marketing services to clients in various industries, including automotive, consumer goods, financial services, and telecommunications. The company is known for its creative and innovative advertising campaigns, including the iconic 'Nothing is Impossible' campaign for Toyota. Saatchi & Saatchi has won numerous awards for its work, including Cannes Lions, Clios, and Effies.
Learn more about Saatchi & Saatchi
Size
6,000 employees
Industry
Media
* Ladders Estimates

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