Data Engineer-Marketing Technology

Foxit•Alpharetta, GA

About The Position

Foxit is seeking an experienced Data Engineer to own data pipelines supporting go-to-market systems. The role is aligned with marketing priorities and works day-to-day within the Business Applications & Data Analytics team, following its development standards, architecture patterns, and code review processes. The engineer will work with demand generation, product marketing, sales operations, and digital teams to understand their needs and build and maintain the pipelines and integrations that support them. This is a hands-on role building and operating infrastructure that connects marketing automation, CRM, data warehouse, licensing, payment, and other source systems. The work supports audience segmentation, attribution measurement, and data-driven campaigns. Responsibilities span pipeline development and operations, data modeling and quality, marketing data and segmentation support, integration development, and reporting and attribution. The role also contributes to Foxit's ongoing data infrastructure investment and platform migration.

Requirements

At least 5 years of data engineering experience, including 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 an equivalent tool.
Hands-on experience with a cloud data platform such as Databricks, Snowflake, BigQuery, or Redshift.
Solid understanding of dimensional data modeling, including fact tables, dimension tables, schema design, and data warehouse concepts.
Experience with medallion or layered data architectures, Kimball-style star schemas, and one-big-table approaches to data modeling.
Working knowledge of API integration patterns, including REST, webhooks, OAuth, and batch synchronization architectures.
Experience with CRM platforms such as Salesforce or HubSpot, marketing automation systems, and CPQ/quoting tools such as DealHub or similar.
Bachelor's degree in Computer Science, Information Systems, or equivalent industry experience.

Nice To Haves

Experience with Databricks, Delta Lake, PySpark, and 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 such as lead scoring, audience segmentation, campaign attribution, and lifecycle stages.
Experience with Azure cloud services such as Azure Functions, Azure DevOps, and Azure Data Factory.
Knowledge of data security and privacy practices, particularly PII handling.
Experience with code review processes and compliance with development standards in a collaborative data engineering team.
Experience operating production data pipelines relied on daily by marketing teams, with awareness of the impact of data freshness and accuracy on campaigns.
Ability to translate requests from non-technical marketing stakeholders into pipeline requirements.
Proactive ownership of pipeline reliability through monitoring and alerting.
Experience handling identity resolution and deduplication across multiple real-world data sources.

Responsibilities

Design, build, and maintain ETL/ELT pipelines across source systems and the Databricks data warehouse, optimizing the existing Bronze → Silver → Gold medallion architecture.
Build pipelines using PySpark and SQL in Databricks notebooks, following established development standards for naming, project structure, and layer-appropriate transformations.
Own data synchronization between Databricks and HubSpot, including enrichment flows into HubSpot and marketing engagement data flowing back into Databricks.
Build and maintain Exchange layer pipelines that format and validate data for external systems.
Build and maintain scheduled batch jobs and event-driven integrations using APIs, REST, webhooks, and OAuth.
Monitor pipeline health, implement alerting for failures and data-quality degradation, and handle incidents when synchronization breaks.
Document data flows, integration architecture, and troubleshooting runbooks.
Build and maintain dimensional models in Databricks, including fact, dimension, and bridge tables, using the team's object definitions and naming standards.
Collaborate with stakeholders and data analysts to build curated, business-ready tables and datamarts with business logic, KPI calculations, and analytics- and campaign-ready aggregations.
Implement identity-resolution and deduplication logic to produce unified customer profiles from multiple systems.
Establish validation rules, quality checks, and monitoring to ensure marketing data accuracy and freshness.
Normalize disparate sources into centralized schemas with type enforcement, deduplication, and null handling.
Support audience segmentation using firmographic, behavioral, and engagement data.
Build data layers supporting lifecycle marketing, triggered campaigns, dynamic journey branching, and profile-based personalization.
Provide marketing and demand generation teams with reliable, accessible HubSpot audience data.
Maintain email deliverability, subscription-management, and suppression-list data flows.
Build and maintain API integrations among marketing, sales, and operational systems using Python and SQL.
Implement field-level transformations, synchronization orchestration, and error handling for system-to-system data flows.
Support website form and lead-capture flows into HubSpot and Databricks.
Integrate data from third-party firmographic, intent, and technographic enrichment providers into automated workflows.
Build and maintain infrastructure for campaign attribution, channel-performance analysis, and funnel reporting.
Ensure accurate data for conversion analytics, lead-source tracking, and marketing ROI measurement.
Route marketing engagement data into Databricks to support centralized cross-functional reporting.