Data Engineer-Marketing Technology

United StatesRemoteFull-timemid

Data EngineerData

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Quick Summary

Key Responsibilities

Design, build, and maintain ETL/ELT pipelines using a medallion architecture (Bronze, Silver, and Gold) to integrate data from Salesforce, HubSpot, NetSuite, Stripe, DealHub,

Requirements Summary

You are an experienced data engineering professional who combines strong technical expertise with a practical understanding of business needs. You take ownership of production pipeline reliability,

Technical Tools

Data EngineerData

Join a collaborative engineering team responsible for building and maintaining the data infrastructure that powers marketing operations and go-to-market strategy. In this hands-on role, you will develop reliable data pipelines and integrations connecting CRM, marketing automation, financial, licensing, and other business systems. Using modern technologies such as Databricks, PySpark, Python, and SQL, you will transform complex data into accurate, actionable insights. Your work will enable smarter audience segmentation, personalized campaigns, lifecycle marketing, and reliable attribution reporting. Partnering with marketing, sales operations, product marketing, and analytics stakeholders, you will translate business needs into scalable technical solutions. This is an opportunity to shape an evolving data platform, improve data quality, and make a measurable impact on marketing performance within a fast-growing global software environment.

As a Data Engineer specializing in Marketing Technology, you will own the development, operation, and continuous improvement of data pipelines that connect business systems and support data-driven marketing initiatives. Your responsibilities will include:

Design, build, and maintain ETL/ELT pipelines using a medallion architecture (Bronze, Silver, and Gold) to integrate data from Salesforce, HubSpot, NetSuite, Stripe, DealHub, learning management systems, and other sources into Databricks.
Develop data transformations using Python, PySpark, and SQL while following established engineering standards, architectural patterns, and code review processes.
Manage data synchronization between Databricks and HubSpot, enriching marketing records with licensing, renewal, subscription, and firmographic information while bringing engagement data back into the data warehouse.
Build and maintain Exchange layer pipelines that prepare, validate, and format data for consumption by external systems.
Develop scheduled batch jobs and event-driven integrations using REST APIs, webhooks, OAuth, and other relevant integration patterns.
Monitor pipeline performance, implement alerts for failures and data quality issues, and lead incident resolution when integrations break.
Document data flows, integration architecture, operational procedures, and troubleshooting runbooks.
Build and maintain dimensional data models, including fact, dimension, and bridge tables, following established warehouse standards.
Collaborate with stakeholders and analysts to create curated datasets and data marts that support business logic, KPI calculations, reporting, and campaign activation.
Implement identity resolution, deduplication, normalization, and validation processes to create consistent customer profiles across multiple systems.
Establish data quality checks, freshness monitoring, type enforcement, and null-handling rules to maintain reliable data across marketing platforms.
Develop the data infrastructure required for audience segmentation using firmographic, behavioral, and engagement signals.
Enable lifecycle marketing through enriched customer profiles, triggered campaigns, dynamic customer journeys, and personalized communications.
Maintain data flows supporting email deliverability, subscription preferences, and suppression list synchronization.
Integrate website forms, lead capture systems, and third-party enrichment providers into automated marketing and analytics workflows.
Support campaign attribution, channel performance analysis, funnel reporting, conversion analytics, lead source tracking, and marketing ROI measurement.
Collaborate with marketing, sales, IT, and analytics teams to translate business requirements into scalable, maintainable data solutions.

Requirements

~2 min read

You are an experienced data engineering professional who combines strong technical expertise with a practical understanding of business needs. You take ownership of production pipeline reliability, value data accuracy, and can translate requests from non-technical stakeholders into robust engineering solutions. The ideal candidate will bring the following qualifications:

At least 5 years of experience in data engineering, including hands-on development and production operation of data pipelines.
Strong proficiency in SQL and Python/PySpark for data processing and pipeline development.
Experience building and maintaining ETL/ELT workflows using Databricks, dbt, Airflow, Azure Data Factory, or equivalent tools.
Hands-on experience with cloud data platforms such as Databricks, Snowflake, BigQuery, or Amazon Redshift.
Solid understanding of dimensional data modeling, including fact and dimension tables, schema design, and data warehouse concepts.
Experience with medallion or layered data architectures, Kimball-style star schemas, and one-big-table modeling approaches.
Working knowledge of API integration patterns, including REST, webhooks, OAuth, and batch synchronization.
Experience with CRM platforms such as Salesforce or HubSpot, marketing automation systems, and CPQ or quoting tools such as DealHub or similar solutions.
Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent professional experience.

Experience with Databricks technologies, including Delta Lake, PySpark, and Unity Catalog.
Familiarity with HubSpot APIs, data structures, and integration requirements.
Experience implementing identity resolution and customer deduplication across multiple source systems.
Understanding of marketing data concepts, including lead scoring, audience segmentation, campaign attribution, and customer 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 the handling and protection of personally identifiable information (PII).
Familiarity with collaborative engineering workflows, code reviews, and compliance with development standards.
Strong problem-solving skills, attention to detail, and a proactive approach to monitoring and maintaining production data systems.
Ability to communicate effectively with technical teams and non-technical stakeholders across marketing, sales, and IT.