Data Engineer, PMTS

We are seeking a highly skilled and experienced Principal Member of Technical Staff (PMTS) to join our dynamic team. This unique role combines deep expertise in data engineering and data science with strong software engineering principles, all within the context of the Salesforce ecosystem. The ideal candidate will be instrumental in designing, building, and deploying robust data pipelines, developing advanced analytical models, and integrating data-driven solutions directly into our Salesforce platform and related applications. This role requires a pragmatic leader who can bridge the gap between complex data challenges and scalable, production-ready software solutions. Responsibilities Advanced Data Modeling & Architecture: Lead the design and implementation of sophisticated data models, applying best practices for scalability, performance, and data integrity. Architect data solutions that support complex analytical requirements and large-scale data transformations, ensuring adherence to enterprise data governance standards. High-Throughput Data Processing & Transformation: Develop, optimize, and manage highly efficient ETL/ELT pipelines capable of processing high volumes of data with low latency. Orchestrate complex data workflows for seamless data integration from diverse sources, ensuring data quality and consistency. Data-Driven Security Solutions: Design and implement data models and solutions that integrate security considerations, including role-based access controls and data auditing, to ensure data privacy, compliance, and governance. Collaborate on defining and implementing data schemas for security events, aligning with industry best practices for secure data handling. Scale & Performance Optimization: Drive initiatives to optimize query performance and enhance the overall scalability and reliability of data infrastructure. Implement solutions for near-real-time analytics and monitoring to ensure high availability and responsiveness of critical data systems. Data Modeling & Analysis: Develop, validate, and deploy advanced statistical models, machine learning algorithms, and predictive analytics solutions to extract actionable insights from large and complex datasets. Software Engineering for Data Products: Write clean, maintainable, and well-tested code to integrate data science models and data pipelines into production systems, including Salesforce applications, APIs, and microservices. Salesforce Integration: Leverage Salesforce platform capabilities (e.g., Apex, Visualforce, Lightning Web Components, Salesforce APIs, Heroku, MuleSoft) to ensure seamless data flow, model deployment, and user experience for data-driven features. Mentorship & Leadership: Provide technical leadership, guidance, and mentorship to junior engineers and scientists, fostering a culture of technical excellence and innovation. Cross-Functional Collaboration: Partner closely with product managers, business stakeholders, and engineering teams to understand requirements, translate business problems into technical solutions, and drive data strategy. Innovation & Research: Stay abreast of the latest industry trends, tools, and technologies in data engineering, data science, and software development, particularly as they relate to cloud platforms and Salesforce. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field. 12+ years of experience in data engineering, data science, or a related software engineering role with proven track record of handling large scale data warehouse and critical workloads supporting high throughput and low latency data pipelines. Proven expertise in designing, building, and maintaining large-scale data pipelines using technologies like Spark, Hadoop, Kafka, Flink, or similar. Strong proficiency in at least one programming language commonly used for data manipulation and analysis (e.g., Python, Scala, Java). Solid understanding of statistical modeling, machine learning algorithms, and their practical application. Experience with relational databases (SQL) and NoSQL databases. Demonstrated experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services. Significant hands-on experience with the Salesforce platform, including: Salesforce data model and platform capabilities, Developing with Apex, Visualforce, or Lightning Web Components (LWC.) Working with Salesforce APIs (REST, SOAP, Bulk.) Understanding of Salesforce integration patterns. Ability to translate complex data findings and technical concepts into clear, concise, and actionable insights for both technical and non-technical audiences. Excellent problem-solving skills, attention to detail, and a results-oriented mindset. Understanding of data privacy regulations (e.g., GDPR, CCPA). Preferred Qualifications PhD in a quantitative field. Experience with Salesforce-specific data tools and products (e.g., Tableau CRM, Salesforce Data Cloud, Marketing Cloud Personalization/Evergage, MuleSoft). Familiarity with containerization technologies (Docker, Kubernetes, Terraform). Experience with CI/CD pipelines and DevOps practices for data and software products. Contributions to open-source projects or a strong portfolio of personal projects. Salesforce certifications (e.g., Salesforce Certified Platform Developer, Salesforce Certified Data Architecture and Management Designer). Experience with stream processing and real-time analytics (with the use of tools like Tableau and CRMA) Domain expertise in data cloud scale for security (w/ various cyber security frameworks like OCSF).

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