Director of Software Engineering (Data)

Salesforce•San Francisco, CA

1d•$197,300 - $344,700•Hybrid

About The Position

Salesforce is seeking a Director of Software Engineering to lead their Data Engineering team. This role will be responsible for the technical strategy and execution of enterprise-scale data ingestion platforms that support analytics, operational workflows, ML models, AI agents, and business applications. The position involves leading a team of senior data engineers and driving the architecture of high-volume, high-throughput data ingestion systems. The Director will set engineering direction for data ingestion from over 100 enterprise systems into Snowflake and Data360, utilizing technologies such as MuleSoft, Informatica, Spark, dbt, Iceberg, Kafka, and Airflow. The goal is to ensure the data ingestion platform is scalable, reliable, governed, secure, and user-friendly for internal teams. This is a director-level role with direct people management responsibilities.

Requirements

15+ years of data engineering experience with focus on data ingestion, including significant experience designing cloud-based data warehouse and lakehouse platforms.
8+ years of engineering leadership and people management experience, ideally managing senior and staff-level engineers.
Proven experience architecting and operating high-volume, high-throughput enterprise data ingestion platforms.
Deep hands-on experience with Snowflake, including data architecture, performance optimization, security, governance, and cost management.
Strong expertise with Apache Spark for large-scale distributed data processing.
Hands-on experience designing lakehouse architectures using Apache Iceberg or comparable open table formats.
Strong experience with Apache Kafka and event-driven, streaming-data architectures.
Deep proficiency in SQL and at least one general-purpose programming language, preferably Python.
Strong understanding of data modeling, distributed systems, schema evolution, data contracts, and batch and streaming processing patterns.
Proficiency with infrastructure as code and CI/CD for data workflows.
Experience implementing enterprise data quality, metadata management, lineage, observability, access control, and governance practices.
Track record of establishing engineering standards and delivering reliable platforms across multiple teams.
Experience leading complex technical programs involving multiple systems, stakeholders, and engineering teams.
Excellent communication and stakeholder-management skills, with the ability to drive alignment across engineering and business leadership.

Nice To Haves

Experience with Salesforce data ecosystems (Data360) within a complex enterprise data ecosystem.
Experience developing AI agents or agentic workflows for data ingestion, transformation, data quality, metadata management, or platform operations.
Experience with agentic data ingestion architectures that can discover sources, interpret schemas, generate pipelines, detect failures, or recommend remediation.
Experience building self-service data platforms, including reusable ingestion frameworks, developer portals, APIs, templates, and paved-road workflows.
Experience designing platform capabilities that allow teams to onboard data sources safely without ongoing involvement from the core Data Engineering team.

Responsibilities

Define and execute the technical strategy and roadmap for enterprise data ingestion platforms.
Architect, build, and operate high-volume, high-throughput batch and real-time data ingestion systems.
Lead the design of scalable data pipelines and ETL/ELT workflows using technologies such as MuleSoft, Informatica, Spark, dbt, Iceberg, Kafka and Airflow.
Design and optimize data models and storage architectures across Snowflake, Data360 and lakehouse environments.
Partner with the Data Platform team to establish Apache Iceberg-based patterns for open, scalable, and interoperable data storage.
Ensure data ingestion platforms meet enterprise requirements for availability, performance, security, observability, data quality, and disaster recovery.
Drive platform scalability through automation, reusable frameworks, standardized ingestion patterns, and self-service capabilities.
Establish and enforce engineering best practices, including architecture reviews, testing, code reviews, CI/CD, documentation, and operational readiness.
Evaluate and champion new data technologies, frameworks, and architectural patterns.
Maintain a strong balance between strategic technical leadership and hands-on involvement in critical architectural decisions.
Directly manage senior data engineers, providing mentorship, coaching, and career development support.
Set clear goals and expectations, conduct regular one-on-one meetings, and lead performance and talent reviews.
Build a collaborative, inclusive, accountable, and high-performing engineering culture.
Develop technical leaders and create growth paths for senior and staff-level engineers.
Partner with recruiting and engineering leadership to grow the team as business needs evolve.
Establish effective team structures, ownership models, and operating mechanisms.
Partner with Product, Data Platform, Architecture, Analytics, Data Science, Security, Governance, and business stakeholders to align priorities and technical direction.
Translate complex business and data requirements into pragmatic technical strategies and execution plans.
Represent Data Ingestion Platform in roadmap, investment, capacity-planning, and architecture discussions with senior leaders.
Establish clear service-level objectives and operating processes for support and incident management.
Collaborate with data producers, consumers, trust and governance teams to improve data contracts, governance, lineage, discoverability, quality, and usability.
Communicate architectural decisions, tradeoffs, risks, and delivery progress to both technical and non-technical stakeholders.