Analytics Engineer

Job Title : Analytics Engineer, Manager level.


Location : Any of mentioned location


Nature of Job : Hybrid, would require to go onsite 4 of days/week


Interview Mode: Video and one Onsite Interview at one of Sapient office location is a must


Project Overview : This individual will work with Publicis Sapient's internal marketing team, specifically in their data function. This individual will help provide support to a well-established data engineer on the team - assisting with data ingestion, pipeline creation, working with APIs, and data modelling.



Top Skills Required :
As an Analytics Engineer, you will design, build, and maintain scalable data pipelines and solutions that transform data from complex marketing and enterprise systems into reliable, well-structured datasets. You will work across APIs, cloud data platforms, and sources such as Salesforce to ensure data is accurately ingested, integrated, modelled, documented, and made available for downstream analytics, reporting, and advanced modelling. You will also leverage modern AI tools to enhance engineering workflows and help build well-documented, AI-ready data assets that support evolving business and technology needs.



Your Impact:

• Design, build, and maintain scalable, reliable data pipelines that ingest and integrate data from marketing platforms, Salesforce, APIs, and other enterprise data sources.
• Develop data transformations and models that provide trusted, well-structured datasets for downstream analytics, reporting, dashboarding, and advanced modelling.
• Build and maintain API-based integrations and custom Python solutions to support reliable and automated data ingestion.
• Ensure data quality, integrity, and availability through validation, testing, monitoring, and troubleshooting of data pipelines and integrations.
• Create and maintain technical documentation, metadata, data definitions, and lineage to improve the discoverability, usability, and governance of data assets.
• Build and structure datasets and documentation to support AI and LLM use cases, while leveraging AI tools throughout development, testing, troubleshooting, and documentation workflows.

Your Skills & Experience

• 5+ years of experience in analytics engineering or a similar data-focused engineering role.
• Strong SQL and Python skills, with experience building and maintaining production-grade data pipelines.
• Hands-on experience with at least one modern cloud data platform such as Snowflake, Databricks, or BigQuery.
• Experience integrating data through REST APIs and other programmatic data sources, including authentication, pagination, error handling, schema changes, and monitoring.
• Experience working with Salesforce data and understanding its underlying data structures and relationships.
• Experience working with marketing data and marketing technology ecosystems, including integrating data across CRM, campaign, digital, media, or customer platforms.
• Strong experience designing pipelines and data models specifically for downstream analytics, dashboarding, reporting, and analytical modelling.
• Understanding of data modelling, warehousing, data quality, governance, and production data engineering best practices.
• Experience with data validation, testing, monitoring, troubleshooting, and maintaining reliable production pipelines.
• Experience creating and maintaining technical documentation, metadata, data definitions, and lineage so datasets are understandable and discoverable.
• Proficiency with modern AI tools and demonstrated ability to incorporate them into day-to-day engineering work, including development, debugging, testing, analysis, and documentation.

Set Yourself Apart With:

• Hands-on experience with Rivery and/or Boomi for data ingestion, integration, and pipeline development.
• Experience preparing datasets, metadata, documentation, or knowledge assets for consumption by AI/LLM applications, including RAG or agent-based use cases.
• Experience with BI and visualization platforms such as Power BI, Tableau, or Looker.
• Experience with advanced analytics, predictive modeling, or machine learning.
• Experience with customer identity resolution, marketing attribution, campaign measurement, or other complex marketing data models.
• Experience with data privacy, consent management, and governance of customer/marketing data.