Staff Data Engineer

Salesforce

. Set the architecture and roadmap for the metrics platform, from ingestion and canonical datasets to the semantic layer and serving .

Posted 10/30/2026full-timeUnited StatesLead💰 $197,300 - $313,700 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in data architecture, data modeling, and metadata management, with a strong focus on building scalable data models and pipelines. Proficient in leading complex technical initiatives and mentoring teams while ensuring alignment across cross-functional groups.

Highest-signal resume keywords

Data ArchitectureData ModelingDeep SQL ExpertisePython ProgrammingExperience with Spark

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data EngineeringMetadata ManagementDimensional ModelingStar/Snowflake ModelingAI-Assisted DevelopmentBuilding APIsMetric CertificationGovernanceData Pipeline DesignTechnical Leadership

Soft Skills

MentoringDriving AlignmentCollaboration

Tools & Technologies

SparkTrinoAirflowIcebergSnowflakeDbtSqlmeshDatahubCubeLookML

Certifications & Qualifications

Related Technical Degree

Industry Keywords

Metrics PlatformSemantic LayerData CatalogsLineage ToolsExperimentation Platforms

Tech Stack

Tools & technologies

AirflowJavaPythonScalaSnowflakeSparkSQLGo

About the role

Key responsibilities & impact

Set the architecture and roadmap for the metrics platform, from ingestion and canonical datasets to the semantic layer and serving
Make tradeoffs between freshness, accuracy, reliability, and cost
Design and scale data models and pipelines that turn billions of records into trusted datasets
Establish measurable quality, predictable SLAs, and freshness signals for consumers
Evolve the semantic layer
Build APIs and tools that let people and AI agents navigate metric definitions, lineage, and ownership, and draft accurate queries
Lead metric certification, governance, and design reviews
Mentor senior engineers
Serve as technical lead for Agentic Analytics on the metrics side
Partner with Product, Data Science, Analytics, and Engineering
Use AI-assisted development to deliver faster without compromising quality

Requirements

What you’ll need

10+ years of software or data engineering experience
6+ years hands-on with data architecture, data modeling, and metadata management
Experience leading complex, multi-team technical initiatives as an individual contributor
Ability to drive alignment across Engineering, Product, Data Science, and Analytics without formal authority
Deep SQL expertise
Strong Python skills
Working proficiency in Go, Java, or Scala
Record of architecting scalable, cost-efficient data models and pipelines
Hands-on experience with Spark, Trino, Airflow, Iceberg, and Snowflake
Experience with dimensional and star/snowflake modeling
Related technical degree required
Experience building infrastructure or tools for AI agents, such as Model Context Protocol (MCP) servers, tool-use APIs, or evaluation harnesses
Experience with semantic layers, metrics platforms, data catalogs, or lineage tools such as dbt, sqlmesh, Datahub, Cube, or LookML
Experience with experimentation platforms, including metrics, exposure logging, and guardrails for A/B testing

Benefits

Comp & perks

Time off programs
Medical insurance
Dental insurance
Vision insurance
Mental health support
Paid parental leave
Life insurance
Disability insurance
401(k)
Employee stock purchasing program
Incentive compensation, equity, and benefits may be available, as applicable
AI agents to accelerate impact
Reasonable accommodations during the application or recruiting process