Summary
✨ AI‑Generated
An analytics engineering role focused on transforming complex enterprise and marketing data into reliable, analytics-ready assets. You will build Python-based ingestion solutions and APIs, develop data models and transformations, maintain quality and lineage, and use modern AI tools to improve engineering workflows.
Highlights
Build scalable data pipelines and analytics-ready datasets across enterprise data sources. The role combines modern data engineering, API integrations, data governance, and AI-assisted development practices.
Description
Analytics Engineer
We are seeking an Analytics Engineer to design, build, and maintain scalable data pipelines that transform complex marketing and enterprise data into reliable, analytics-ready datasets.
This role will work across APIs, cloud data platforms, Salesforce, and marketing technologies while leveraging modern AI tools to improve engineering workflows and develop well-documented, AI-ready data assets.
Key Responsibilities
Build and maintain scalable data pipelines integrating Salesforce, marketing platforms, APIs, and other enterprise data sources.Develop data transformations and models for analytics, reporting, dashboards, and advanced modeling.Build API integrations and Python-based solutions for automated data ingestion.Ensure data quality through testing, validation, monitoring, and troubleshooting.Maintain data documentation, metadata, definitions, and lineage to support governance and discoverability.Leverage AI tools across development, testing, debugging, analysis, and documentation workflows.Qualifications
5+ years of experience in analytics engineering or a similar data engineering role.Strong SQL and Python skills with experience developing production-grade data pipelines.Experience with modern cloud data platforms such as Snowflake, Databricks, or BigQuery.Hands-on experience with REST APIs, including authentication, pagination, error handling, and schema changes.Experience working with Salesforce data and marketing technology ecosystems, including CRM, campaign, digital, media, or customer data.Strong understanding of data modeling, warehousing, data quality, governance, and production engineering practices.Experience with data documentation, metadata, definitions, and lineage.Proficiency with modern AI tools and their application in day-to-day engineering workflows.Nice to Have
Experience with Rivery and/or Boomi.Experience preparing data and metadata for AI/LLM, RAG, or agent-based applications.Experience with Power BI, Tableau, or Looker.Background in advanced analytics, predictive modeling, or machine learning.Experience with customer identity resolution, marketing attribution, campaign measurement, or marketing data models.Knowledge of data privacy, consent management, and customer data governance.
