The Team
The Metrics Foundations team is part of Slack's Data Engineering organization within Data & Insights and works closely with Product, GTM, Data Science, and Analytics teams to ensure Slack remains a data-driven company. Data Engineering builds and operates the infrastructure that powers Product, Analytics, Marketing, Sales, and Operations, serving as a foundational pillar for Slack and one of the most highly leveraged teams in the company.

The Metrics Foundations team builds trusted, reusable datasets and metrics that enable fast, reliable, data-informed decision-making. We partner with Product, Data Science, Analytics, and Engineering teams to define and manage critical product and business metrics through centralized governance, ensuring high data quality, consistency, and adherence to targeted SLAs (service-level agreements).

The Experience
As a Senior Data Engineer, you play a critical role in building the trusted data foundation that powers decision-making across Slack. You champion data quality, governance, and reliability while designing and scaling data models, pipelines, and metric systems that provide consistent and timely access to business insights.

You work cross-functionally with business domain experts, Product, Analytics, Data Science, and Engineering teams to understand complex business problems and translate them into scalable data models and technical solutions. You design, build, and optimize data pipelines that transform billions of records into trusted, actionable datasets and metrics.

You lead initiatives that establish and evolve data governance and management practices, improve the information lifecycle, and standardize critical company metrics. You also provide technical leadership and mentorship, helping teams across Slack build high-quality, reliable, and reusable data products.

The ideal candidate brings deep technical expertise, strong business acumen, and a track record of operating effectively in complex and evolving data ecosystems. You are a self-starter who thrives on ambiguity, takes end-to-end ownership, and is passionate about building data foundations that have broad organizational impact.

What You'll Actually Be Doing

• Partner closely with Product teams and business stakeholders to identify high-impact questions and translate business needs into scalable data models, metrics, analyses, and technical solutions
• Partner with business domain experts, Data Analytics, Data Science, and Engineering teams to build foundational datasets that are trusted, well understood, aligned with business strategy, and enable self-service analytics
• Design, build, and scale data models and pipelines that integrate and transform data from multiple sources into trusted, accessible datasets with measurable quality and predictable SLA performance
• Own the end-to-end lifecycle of metrics, analytical models, and data products, from initial exploration and prototyping through production, adoption, and ongoing maintenance
• Leverage AI-assisted development to accelerate engineering productivity while maintaining high standards for code, data quality, and maintainability, and build data foundations that enable automation and AI-native insights and decision-making
• Lead and influence the data strategy across multiple teams, domains, and use cases, driving alignment on scalable technical foundations and long-term investments
• Drive initiatives that expand access to trusted company metrics, enabling self-service analytics and faster, more consistent decision-making across Slack
• Establish, document, and promote data engineering best practices across Slack
• Mentor engineers and provide hands-on technical guidance, helping raise the technical bar across the organization

You're Our Person If...

• You have 8+ years of overall software engineering or data engineering experience, including 5+ years of hands-on experience with data architecture, data modeling, data management, and metadata management
• You can independently structure and own ambiguous, high-impact problems from initial framing through technical strategy, execution, and measurable outcomes
• You bring strong autonomy, resourcefulness, and creativity when navigating complex technical, operational, and stakeholder constraints
• You have deep expertise in SQL and proven experience designing scalable data pipelines and data transformations that operate reliably across large and complex datasets
• You have a proven track record of architecting and optimizing data models, schemas, and processing workflows to improve performance, scalability, cost efficiency, and reliability in modern data warehouse environments
• You can influence technical direction and drive alignment across Engineering, Product, Data Science, Analytics, and business stakeholders without relying solely on formal authority
• You are proficient in at least one programming language commonly used in Data Engineering, such as Python or Java
• You have hands-on experience with large-scale data technologies and platforms such as Snowflake, Spark, Airflow, and Hive
• You have experience working with relational and NoSQL data stores and a range of data modeling approaches, including logging, columnar, star and snowflake schemas, and dimensional modeling
• You are familiar with data governance frameworks, software development lifecycle (SDLC) practices, and Agile methodologies
• You have excellent written and verbal communication skills, with the ability to influence and collaborate effectively with technical and business stakeholders at all levels

Even Better If...

• You have a Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent training, fellowship experience, or relevant professional experience