Data Engineer-Marketing Technology

Foxit is hiring a Data Engineer to build and operate data pipelines and integrations that support its marketing and go-to-market systems. The role connects marketing, CRM, operational systems, and Databricks to enable audience segmentation, campaign measurement, and data-driven marketing.

Responsibilities:

• Design, build, and maintain ETL/ELT pipelines, building upon and further optimizing our existing medallion architecture (Bronze → Silver → Gold) to move data between source systems (Salesforce CRM, HubSpot, NetSuite, Stripe, DealHub, LMS) and our Databricks data warehouse
• Build pipelines using PySpark and SQL in Databricks notebooks, following established development standards for naming, project structure, and layer-appropriate transformations
• Own the data sync layer between Databricks and HubSpot — enrichment flows inbound to HubSpot (license status, renewal dates, subscription state, firmographic data) and marketing engagement data flowing back to Databricks (email events, workflow enrollment, lifecycle changes)
• Build and maintain Exchange layer pipelines that curate data for external system consumption, formatting and validating data to meet target system requirements
• Build and maintain scheduled batch jobs and event-driven integrations using APIs (REST, webhooks, OAuth)
• Monitor pipeline health, set up alerting for failures and data quality degradation, and own incident response when syncs break
• Maintain documentation of data flows, integration architecture, and troubleshooting runbooks
• Build and maintain dimensional models in Databricks (fact tables, dimension tables, bridge tables) following our data warehouse object type definitions and naming standards
• Work in collaboration with stakeholders and data analysts to build curated, business-ready tables and datamarts that apply business logic, KPI calculations, and aggregations optimized for analytics and campaign activation
• Implement identity resolution and deduplication logic to produce unified customer profiles from multiple source systems
• Establish data validation rules, quality checks, and monitoring to ensure accuracy and freshness of data flowing into marketing systems
• Normalize disparate data sources into clean centralized schemas with proper type enforcement, deduplication, and null handling
• Ensure the data infrastructure supports audience segmentation, including firmographic, behavioral, and engagement signals
• Build the data layer that powers lifecycle marketing - triggered campaigns, dynamic journey branching, and personalization based on enriched customer profiles
• Support marketing and demand gen teams with reliable, accessible data for building audience targets in HubSpot
• Maintain data flows for email deliverability, subscription management, and suppression list synchronization
• Build and maintain API integrations between marketing, sales, and operational systems using Python and SQL
• Implement field-level transformation logic, sync orchestration, and error handling for system-to-system data flows
• Support website form and lead capture data flows - ensuring clean handoff from web properties into HubSpot and Databricks
• Work with third-party enrichment providers (firmographic, intent, technographic) to integrate enrichment data into automated workflows
• Build and maintain the data infrastructure that supports campaign attribution, channel performance analysis, and funnel reporting
• Ensure accurate data for conversion analytics, lead source tracking, and marketing ROI measurement
• Support centralized reporting by routing marketing engagement data back into Databricks for cross-functional analysis

Requirements:

• 5+ years of experience in data engineering, with hands-on pipeline development and production operations
• Strong proficiency in SQL and Python/PySpark for data pipeline development
• Experience building and maintaining ETL/ELT pipelines using Databricks, dbt, Airflow, Azure Data Factory, or equivalent
• Hands-on experience with cloud data platforms - Databricks, Snowflake, BigQuery, or Redshift
• Solid understanding of dimensional data modeling - fact tables, dimension tables, schema design, and data warehouse concepts
• Experience with medallion or layered data architectures (raw → cleansed → business-ready), Kimball-style star schemas, and one-big-table approaches to data modeling
• Working knowledge of API integration patterns - REST, webhooks, OAuth, batch sync architectures
• Experience with CRM platforms (Salesforce, HubSpot, or similar), marketing automation systems, and CPQ/quoting tools (DealHub or similar)
• Bachelor's degree in Computer Science, Information Systems, or equivalent industry experience
• Experience with Databricks (Delta Lake, PySpark, Unity Catalog)
• Familiarity with HubSpot APIs and data model
• Experience with identity resolution and customer data deduplication across multiple source systems
• Exposure to marketing data concepts — lead scoring, audience segmentation, campaign attribution, lifecycle stages
• Experience with Azure cloud services (Azure Functions, Azure DevOps, Azure Data Factory)
• Knowledge of data security and privacy practices, particularly regarding PII handling
• Experience with code review processes and development standards compliance in a collaborative data engineering team
• You've built and operated production data pipelines that marketing teams depend on daily - you understand the impact of data freshness and accuracy on campaign execution
• You're comfortable working within a marketing department and can translate data requests from non-technical stakeholders into pipeline requirements
• You take ownership of pipeline reliability - building monitoring and alerting proactively rather than waiting for someone to report a problem
• You've worked with multiple data sources and know how to handle the messiness of real-world identity resolution and deduplication