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Data Engineer Cdp Jobs in Missouri (NOW HIRING)

Capstone IT

Principal Data Engineer

Springfield, MO · On-site

Principal Data Engineer *** Please note, this role is not open to third party candidates or ... CDP) and omnichannel analytics initiatives Strong understanding of Google Analytics, customer ...

FocusKPI

Marketing Operations Analyst

Columbia, MO · On-site

Data Integration: Collaborate with cross-functional teams such as IT and engineering to integrate data from various sources into the customer data platform (CDP) for comprehensive analysis.

PATTERNS

AEM Product Manager

Springfield, MO · On-site

Partner with engineering teams to ensure scalable, high-performance AEM implementations. * Work ... Adobe Audience Manager / CDP * Experience managing AEM Sites and Digital Asset Management (DAM ...

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Data Engineer Cdp information

What is a data engineer CDP?

Data Engineer CDPs (Customer Data Platforms) are professionals who specialize in designing, building, and maintaining data infrastructure for customer data platforms. They are responsible for collecting, processing, and integrating large volumes of customer data from multiple sources to ensure that the data is accurate, clean, and accessible for marketing, analytics, and business intelligence. Their work enables organizations to create unified customer profiles and deliver personalized experiences across channels. Data Engineer CDPs often work closely with data scientists, analysts, and marketers to support data-driven strategies and compliance with privacy regulations.

How do data engineer CDPs typically collaborate with marketing and analytics teams?

Data Engineers in CDP environments frequently collaborate with marketing and analytics teams to ensure data pipelines are optimized for campaign personalization and customer insights. They translate business requirements into technical solutions, enabling real-time data integration and accessibility for non-technical users. Regular communication is essential to align on data quality, privacy compliance, and the evolving needs of user segmentation, making teamwork and clear documentation key to success in this role.

What are the key skills and qualifications needed to thrive as a data engineer CDP, and why are they important?

To thrive as a Data Engineer CDP, you need strong expertise in data modeling, ETL processes, and database management, often backed by a degree in computer science or a related field. Familiarity with cloud data platforms (such as AWS, Azure, or GCP), experience in programming languages like Python or Scala, and certifications in specific CDP tools (like Cloudera or Snowflake) are typically required. Analytical thinking, problem-solving, and effective communication are critical soft skills for collaborating with cross-functional teams and translating business needs into technical solutions. These skills ensure robust, scalable data architectures and enable efficient data-driven decision-making across the organization.
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Data Engineer Cdp jobs near you

BioTech Data Engineer

Stellar IT Group

Saint Louis, MO • On-site

$111K - $133K/yr

Full-time

Re-posted 2 days ago

Job description

Overview:
Job Title: Biotech Data Engineer
Job Type: FTE
Work Mode: Remote
The Biotech Data Engineer focuses on designing, building, and maintaining scalable Azure Databricks-based data pipelines and architectures that enable analytics, AI/ML, and reporting across commercial functions. It involves leading data engineering initiatives, ensuring governance and compliance, optimizing performance and costs, and collaborating across teams to advance the company's data and AI strategy. The role reports to the Director of Business Intelligence.
Roles & Responsibilities
  • Design, build, and maintain scalable, reliable, and cost-efficient data pipelines using Azure Databricks in support of analytics, machine learning, data science, and operational use cases
  • Lead data engineering initiatives that enable AI/ML model development, LLM integrations, and AI-driven applications while ensuring scalability and alignment with enterprise and business priorities
  • Architect and implement data integration frameworks across diverse Adtech and Martech ecosystems, incorporating Google Analytics, media campaign data, and third-party marketing APIs like Salesforce
  • Manage and optimize data ingestion, transformation, and storage processes using SQL, Python, and PySpark to integrate structured and unstructured data sources
  • Design and maintain API integrations with internal and external systems, including Python- and PySpark-based services and AI/LLM-powered APIs for advanced analytics and automation
  • Administer and maintain Azure-based data tools and platforms (e.g., Databricks, ADF) to ensure operational excellence, reliability, and security
  • Collaborate with internal stakeholders and external partners to evolve data platform design and architecture that supports advanced analytics, personalization, marketing intelligence, and marketing automation
  • Execute against the company's data and AI strategy by translating strategic goals into technical architecture, design and requirement documents, and implementation roadmaps
  • Ensure data quality, integrity, and consistency through robust validation, monitoring, and alerting mechanisms within Azure Databricks
  • Implement and enforce data governance, security, and compliance standards in collaboration with IT, InfoSec, and data governance teams
  • Partner with analytics, marketing, commercial operations, and core technology/cybersecurity teams to deliver fit-for-purpose commercial data products
  • Monitor and optimize data infrastructure costs, performance, and scalability across Azure cloud environments
  • Develop and maintain architecture documentation, pipeline specifications, and design diagrams for transparency and knowledge sharing with technical and business stakeholders
  • Participate in architecture discussions, design reviews, and CI/CD workflows to ensure high-quality engineering and deployment practices as part of an Agile engineering team
  • Continuously evaluate and recommend new Azure services, designs, improvements, frameworks, and AI integration tools to enhance our data platform
  • Drive automation, observability, and standardization across data workflows to improve efficiency and reduce manual intervention
  • Complete all job duties in compliance with company policy, SOPs, safety rules, and applicable federal, state, and local regulations

Education & Licenses And Experience
Bachelor of science degree required. A minimum of 5 years transferable working experience in the area of operational support of a Data Engineering, Data Architecture, or Cloud Platforms function preferred. The ideal candidate will have recent and relevant experience in the pharma or biotech industry.
Required Competencies & Skills
  • Experience with Azure Cloud (Databricks, DevOps, DataFactory)
  • Pharma / biotech domain experience, specifically within the commercial data space (sales, market access / payer, marketing)
  • Strong hands-on python, pyspark, and SQL skills
  • Direct experience with building and leveraging API integrations in ETL pipeline development
  • Experience integrating with current best-in-class AI models and APIs like OpenAI API, Databricks AI models, etc.
  • Self-driven with ability to independently design end-to-end data pipelines while ensuring architectural best practices
  • Ability to collaborate with a broad set of stakeholders to evaluate the business need and construct a technical design that can enable stakeholder priorities
  • Travel up to 20%

Preferred Competencies & Skills
  • Strong knowledge of and experience with maximizing business value using Databricks Unity Catalog or Databricks One capabilities
  • Familiarity with tools and practices in the Martech and Adtech landscape
  • Knowledge of Consumer, Patient, or HCP data ecosystems
  • Experience with identity providers like LiveRamp, Acxiom, Experian, etc.
  • Experience with custom-built or third-party Customer Data Platforms (CDP)
  • Experience with marketing automation tools

Skills:
Data Engineering

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