Senior Technology Lead, Engineering

Expedia•Chicago, IL

About The Position

At Expedia Group, we help travelers explore the world, one journey at a time. As a global travel company powered by passionate people, trusted partnerships, and leading technology, we connect travelers, partners, and advertisers through our consumer brands, B2B network, and travel advertising business. Here, you'll do meaningful work that helps millions of people discover, book, and experience travel with more ease, confidence, and joy. Our five Behaviors-Traveler First, Think Big, Operate with Excellence, Ownership Mindset, and Succeed Together-help foster a supportive environment where people can grow their careers and have the flexibility, benefits, and support to do their best work. Join us and build for travelers everywhere. Ready to help build the technology behind how Expedia Group communicates with its global supply partners? B2B Supply Marketing's Digital Operations (DigOps) team is responsible for all B2B Supply partner marketing communications, the scalable email channel that delivers both campaigns and Next Best Action (NBA) opportunity recommendations to partner contacts. We're in the middle of validating our next-gen MarTech stack with Salesforce Marketing Cloud & Salesforce Data Cloud as the future platforms for partner marketing, consolidating multiple Marketo instances and Message Gears onto the same Salesforce technology stack used across EG. We're looking for a Senior Technology Lead to help drive that build-out. In this role, you'll be a senior technical voice on the SFMC and partner marketing platform effort bringing the mix of Data Engineering, Software Engineering, Architecture, Data Modeling, and developing data ingestion patterns to support the Marketing Programs and AI orchestration . Along with engineering big picture, you will lead design for complex integrations, shape how the team builds and models to empower our marketing team in delivering best-in class marketing campaigns and API systems. You will also help us apply AI tooling – both in how we build and in what we build and help senior engineers level up.

Requirements

Bachelor's degree in Computer Science or a related technical field; or equivalent related professional experience.
10+ years of experience building and operating enterprise software platforms, with hands-on experience in Salesforce Data Cloud (Data 360, Automation Studio, Journey Builder, Content Builder, SSJS/AMPscript, SOAP/REST APIs) or comparable enterprise MarTech platforms.
Demonstrated experience leading complex technical projects end-to-end within a team or domain, with minimal supervision.
Experience with data integration patterns (for example, SFTP, SOAP/REST, Data Extensions, CDP/Data Cloud) connecting marketing platforms to upstream and downstream systems.
Experience architecting data models for CRM or marketing platforms, such as Salesforce Marketing Cloud (Contact Builder/Data Designer, Data Extensions) and/or Salesforce Data Cloud (Data Model Objects, Data Lake Objects, Identity Resolution).
Data engineering experience, including designing and building ETL/ELT pipelines end-to-end, data modeling for large-scale audience and contact datasets, and working with SQL and cloud data warehouses (for example, AWS, Snowflake, or similar) to move and transform partner data into marketing platforms.
Strong proficiency in SQL and at least one programming language commonly used for data engineering (for example, Python, Scala, or Java), along with experience using data processing frameworks and orchestration tools (for example, Spark, Airflow, dbt, or similar).
5+ years of hands-on programming experience with Python, Java, or Scala.
Strong SQL skills and a solid understanding of database design, data ingestion, modeling, and performance optimization.
Demonstrable experience leveraging AWS services, including S3, Redshift, Lambda, Glue, DynamoDB, EC2, and AppFlow.
Experience with batch and/or stream processing using Spark, Kafka, or SQS.
Experience building with LLMs and GenAI services (for example Amazon Bedrock)—prompt design, retrieval/RAG patterns, vector stores, and evaluating output quality, cost, and latency.
Experience using agentic AI for development and task orchestration – configuring, constraining, and reviewing agent-produced code and building custom skills/workflows to accelerate delivery.

Nice To Haves

Experience migrating marketing communications off legacy platforms (for example, Marketo, Message Gears, or similar ESPs) onto Salesforce Marketing Cloud.
Experience with Salesforce Data Cloud (Data 360), including Identity Resolution, Calculated Insights, DMO/DLO modeling, or evaluating native platform data management as an alternative to custom data pipelines.
Experience with Zero Copy data architecture (for example, Salesforce Data Cloud Zero Copy Partner Network/BYOL, or comparable federated query patterns) that lets data be queried in place across an external data lake and a CRM/marketing platform without duplicating it.
Experience designing or building knowledge graphs, including ontology and schema design, entity resolution, and graph databases (for example, Neo4j, Amazon Neptune, or similar) and query languages (for example, Cypher, SPARQL, Gremlin).
Familiarity with CI/CD for Salesforce metadata (Git-based deployment, change/release management for Marketing Cloud components).
Experience in B2B or partner-facing marketing and communications platforms.
Experience designing data pipelines that feed audience segmentation or Next Best Action (NBA) style recommendation systems.
Experience with streaming and batch data processing, including schema evolution, backfill, and data freshness service level objectives (SLOs).
Experience with data governance practices such as data contracts, lineage tracking, and PII/privacy compliance in marketing data platforms.
Salesforce Marketing Cloud certifications (for example, Email Specialist, Developer).

Responsibilities

Lead the design and delivery of complex, end-to-end SFMC initiatives for partner marketing, such as journey and automation builds and NBA integration, with minimal day-to-day guidance.
Own and architect the end-to-end data model for Salesforce Marketing Cloud and Salesforce Data Cloud (Data 360), including Data Extensions, Contact Builder/Data Designer relationships, Data Model Objects (DMOs), Data Lake Objects (DLOs), and Identity Resolution rulesets.
Design and build a knowledge graph connecting partner, property, contact, and campaign entities across SFMC, Data Cloud, and upstream systems to enable unified partner profiles, relationship discovery, and Next Best Action (NBA) recommendations.
Define the ontology, entity-relationship structure, and identity resolution/deduplication logic underpinning the knowledge graph and Data Cloud unified profiles.
Design, develop, and optimize data capture, processing, storage, and distribution solutions that power marketing campaigns, reporting, and partner integrations.
Build and maintain batch and streaming pipelines that are reliable, observable, and cost-efficient at scale.
Model and curate datasets that marketing, analytics, and partner teams can trust and self-serve from.
Expose data through well-designed APIs and services that integrate with internal platforms and third-party marketing tools.
Use AI-assisted development tools in your day-to-day workflow, and help the team establish where they help and where they do not.
Partner with the business to integrate AI and LLM capabilities into marketing data workflows enrichment, classification, content generation, and agentic automation—with appropriate evaluation and guardrails.
Design and develop ETL/ELT pipelines end-to-end, from source system extraction (for example, PMDS, Salesforce Core CRM) through transformation and orchestration, into SFMC Data Extensions, Data Cloud DMOs/DLOs, and the knowledge graph, including evaluating SFMC/Data Cloud-native data management versus custom pipelines.
Architect Zero Copy integration between Salesforce Data Cloud and EG's external data lake (for example, AWS, EGDL), so data used for marketing can be queried in place without duplicating it, keeping it available outside SFMC for analytics and other downstream consumers.
Establish data pipeline orchestration, scheduling, and monitoring (for example, Airflow, dbt, or similar), with clear SLAs for data freshness, identity resolution accuracy, and pipeline reliability, as the team scales from pilot to production volumes.
Drive platform health and reliability practices (for example, change/release management, CI/CD for Salesforce metadata, monitoring and alerting) across SFMC and Data Cloud.
Serve as a go-to technical resource for engineers and partner marketing stakeholders building on SFMC and Data Cloud, unblocking complex technical issues and reviewing designs and pull requests for platform-wide standards.
Mentor other engineers on the team and share platform and data architecture knowledge through documentation to raise the team's overall technical standards.
Partner with partner marketing stakeholders, data science partners, and Salesforce (vendor) contacts to translate platform capabilities and constraints into practical implementation plans.