Senior Data Engineering Leader, Scalable Platforms

Publicis Sapient; Chicago (IL); On-site; USD 160,000 - 262,000; Full time

Benefits offered by this job

Health benefits

Generous paid leave

Wellness program

Employee assistance

Job summary

Publicis Sapient is seeking a Senior Manager Data Engineering to design and implement scalable data platforms enabling data-driven decision making. You will lead end-to-end data systems, batch and streaming pipelines, and AI-enabled data services across cloud platforms, while mentoring a growing team.

You will partner with cross-functional teams, translate client requirements into robust architectures, automate operations, and drive data quality and reliability across production environments.

Qualifications

• End-to-end data pipelines and production‑grade platforms.
• Public cloud data platforms (AWS/Azure/GCP) experience.
• Databricks with Delta Lake or lakehouse patterns.

Responsibilities

• Design and implement scalable data platforms for data-driven decisions.
• Lead large-scale data systems and modernization initiatives.
• Build batch and streaming pipelines on cloud platforms.
• Design data foundations supporting AI-enabled services.
• Automate data platform operations and improve observability and reliability.
• Provide project estimates and feasibility assessments.
• Mentor junior team members and contribute hands-on.

Skills

End-to-end data pipelines

Python proficiency

Cloud platforms

Databricks

SQL/NoSQL databases

Streaming pipelines

MLOps familiarity

Cross-functional collaboration

Tools

Databricks

BigQuery/Redshift

Snowflake

Spark

Job description

Publicis Sapient is seeking a Senior Manager Data Engineering to design and implement scalable data platforms enabling data-driven decision making. You will lead end-to-end data systems, batch and streaming pipelines, and AI-enabled data services across cloud platforms, while mentoring a growing team.

You will partner with cross-functional teams, translate client requirements into robust architectures, automate operations, and drive data quality and reliability across production environments.