Senior Associate Big Data Cloud Engineering - Atlanta Hybrid

Sapient Corporation; Atlanta (GA); Hybrid; USD 107,000 - 130,000; Full time

Benefits offered by this job

Flexible vacation policy

15 paid holidays

Tuition reimbursement

Wellness program

Job summary

Publicis Sapient is seeking a Senior Associate Data Engineer to join our team of technologists. You will lead and deliver large-scale data solutions for digital transformation projects, leveraging cloud data platforms and AI engineering techniques to drive measurable business value.

You will design, build, and optimize batch and streaming pipelines, mentor junior engineers, and collaborate across functions to deliver end-to-end data foundations that power AI-enabled services.

Qualifications

• Experience implementing end-to-end data pipelines and production-grade platforms.
• Hands-on with at least one major cloud data platform (AWS/Azure/GCP).
• Databricks experience preferred (notebook, jobs, Delta Lake).
• Strong Python for data engineering and AI workflows.
• Experience with column-oriented DBs (BigQuery, Redshift, Vertica) and NoSQL (DynamoDB, Cosmos DB).
• Pipelines for streaming and batch using Spark, Dataflow, Glue ETL, etc.
• Data modeling, warehouse design, lakehouse/data mesh patterns.
• Code repositories, CI, testing, release management, production support.
• MLOps familiarity for AI/ML deployment and monitoring.
• Hands-on with cross-functional collaboration and mentoring.

Responsibilities

• Lead and deliver large-scale data solutions for digital transformation projects.
• Design, develop and optimize batch and streaming data pipelines across cloud platforms.
• Support AI-enabled engineering use cases by building scalable data services.
• Mentor junior engineers and contribute hands-on to delivery.
• Translate client requirements into system design and measurable outcomes.

Skills

End-to-end data pipelines

Cloud data platforms

Databricks

Python

Data modeling & warehouse

CI/CD & production support

MLOps fundamentals

Communication & collaboration

Tools

Amazon Web Services

Microsoft Azure

Google Cloud Platform

Databricks

BigQuery

Redshift

DynamoDB

Bigtable

Cosmos DB

SQL Server

Oracle

MySQL

Glue ETL

Lambda

Dataflow

Azure Data Factory

Spark

Spark Streaming

Job description

Job Description

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.

As part of our dedication to an inclusive and diverse workforce, Publicis Sapient is committed to Equal Employment Opportunity without regard for race, color, national origin, ethnicity, gender, protected veteran status, disability, sexual orientation, gender identity, or religion. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at [e-mail]