Data Engineer-Marketing Technology

Foxit is hiring a Data Engineer to build and operate data pipelines and integrations that connect marketing, sales, and operational systems with its Databricks data warehouse. The role supports audience segmentation, lifecycle campaigns, data quality, and marketing attribution in collaboration with marketing and data teams.

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