Lead Data Engineer

Salesforce•Chicago, IL

1d•$172,500 - $260,100•Hybrid

About The Position

As a Data Engineer at Salesforce within the Data & Analytics organization, you will collaborate with cross-functional teams to create and manage robust data solutions that support our analytics and business intelligence initiatives, building scalable and efficient data pipelines, optimizing data workflows, and ensuring data quality and reliability. The Customer Success Data Platform organization sits at the intersection of Automation, Analytics, and AI, pushing the boundaries of Salesforce technology as Customer 0 and providing trusted data that supports the AI + Human tandem.

Requirements

8+ years of experience as a Data Engineer or in a similar role.
A related technical degree required.
Proficiency in data engineering tools and languages, such as Python, SQL, and Spark.
Strong understanding of database concepts, data modeling, and ETL processes with tools like Airflow, dbt, Informatica, etc.
Experience with cloud-based data solutions (e.g., AWS, Azure, Google Cloud).
Familiarity with data warehousing, SQL, NoSQL databases, and data integration techniques.
Experience with the Salesforce Ecosystem, specifically Data Cloud.
Problem-solving skills to troubleshoot and resolve data-related issues.
Excellent communication skills and ability to collaborate in a cross-functional environment.

Responsibilities

Design and oversee the implementation of a robust Medallion Architecture (Bronze, Silver, and Gold layers), ensuring seamless transitions from raw ingestion to highly refined, business-logic-heavy data assets.
Own the development and governance of the Semantic Layer, defining standardized metrics and dimensions to ensure consistent interpretation of KPIs across all downstream business intelligence and reporting platforms.
Lead the transition from flat tables to sophisticated Star and Snowflake schemas. Design high-performance data models that support complex relationship mapping, historical versioning (SCD Type 2), and high-concurrency querying.
Build and maintain scalable pipelines for identity resolution, stitching together disparate data points across the Salesforce ecosystem to create unified, governed customer and product graphs.
Engineer the specialized data structures and features required for advanced attribution and causal inference, ensuring these models are backed by high-integrity, production-ready data.
Establish and enforce strict Data Contracts between upstream producers and downstream consumers. Implement automated validation frameworks to monitor data freshness, schema drift, and semantic accuracy.
Drive engineering best practices by implementing rigorous CI/CD workflows for data pipelines, emphasizing automated testing, version control for SQL/Python transformations, and proactive performance tuning.
Collaborate with Product Managers and Engineering leaders to translate complex business requirements into scalable technical architectures, prioritizing data lineage, transparency, and low-latency access to trusted metrics.