Analytics Data Engineer for AI-Ready Pipelines

Sapient Corporation; Chicago (IL); On-site; USD 120,000 - 160,000; Full time

Job summary

Publicis Sapient is seeking an Analytics Engineer in Chicago to design, build, and maintain scalable data pipelines that ingest data from marketing platforms, Salesforce, and APIs. You will develop transformations and models for analytics, reporting, and advanced modelling, while building API-based integrations and Python solutions for automated data ingestion.

Role focuses on data quality, governance, and documentation, plus enabling AI/LLM use cases with well-structured datasets and metadata.

Qualifications

• 5+ years in analytics engineering or similar data role.
• Proficient in SQL and Python for production pipelines.
• Experience with cloud data platforms (Snowflake/Databricks/BigQuery).
• Experience with Salesforce data and REST APIs.
• Experience with data quality, governance, and documentation.

Responsibilities

• Design and maintain scalable data pipelines ingesting data from marketing platforms, Salesforce, and APIs.
• Develop data transformations and models for analytics, reporting, and modeling.
• Build API-based integrations and Python solutions for automated ingestion.
• Ensure data quality, validation, monitoring, and troubleshooting of pipelines.
• Create metadata, data definitions, and lineage to improve discoverability and governance.
• Support AI/LLM use cases by structuring datasets and documentation.

Skills

SQL

Python

Data pipelines

Data modeling

ETL/ELT

REST APIs

Salesforce data

Cloud platforms

Tools

Snowflake

Databricks

BigQuery

Power BI

Tableau

Looker

Job description

Publicis Sapient is seeking an Analytics Engineer in Chicago to design, build, and maintain scalable data pipelines that ingest data from marketing platforms, Salesforce, and APIs. You will develop transformations and models for analytics, reporting, and advanced modelling, while building API-based integrations and Python solutions for automated data ingestion.

Role focuses on data quality, governance, and documentation, plus enabling AI/LLM use cases with well-structured datasets and metadata.