Job Description

The Slack Data Platform team is looking for a Staff Software Engineer to champion the trust and quality of our data. Our mission is to develop and maintain a dependable, trusted, and well-governed platform that guarantees consistency, accuracy, and timely access, thereby fostering standardization. We're transforming data exploration by utilizing metadata to make data discovery intuitive and insightful.

In collaboration with business experts, analytics professionals, and engineers, you'll drive the Data Quality and Governance roadmap. Our primary objective is a robust and intelligent platform that empowers both internal teams and end-users through enhanced data discoverability and governance. This platform will serve as the foundation for future AI-driven capabilities by enriching data understanding and interaction via a semantic layer and natural language processing. This strategy aims to maximize data value, streamline analytics workflows, and establish a scalable, user-friendly platform for innovation across the organization.

We're seeking a technically strong, product-oriented individual with a proven track record in the data governance space. You're self-motivated and passionate about converting intricate business needs into scalable technical solutions that enable data-driven decisions throughout the company.

In this role, you'll play a crucial part in enabling the entire Slack product organization to embrace a data-driven culture. You'll work closely with product owners, empowering them to configure essential product and business metrics within a framework of centralized governance, ensuring consistently high quality and reliability.

What you will be doing:

1. Lead the design and execution of our Data Governance & Data Quality roadmap, ensuring highly resilient, scalable, and reliable data production and consumption.
2. Identify and pursue opportunities to create new tools that enhance scalability and enable self-service data solutions.
3. Partner with cross-functional teams, including product managers, engineers, data scientists, and business stakeholders, to align on data requirements and drive the development of scalable end-to-end systems.
4. Contribute to the development of long-term data strategies and roadmaps, and influence the technical direction of data engineering practices within the organization.
5. Mentor and coach team members, providing guidance and support to enhance their skills and performance.

What you should have:

• 7+ years of relevant industry experience with a Bachelors and/or Masters degree in Computer Science, Engineering, a related field, or equivalent training, fellowship, or work experience.
• Extensive experience designing, and leading implementation of scalable, low-latency systems for metadata, data quality, data catalogs, data discovery.
• Proficiency in object-oriented and/or functional programming languages (Python, Java/Scala, Go) and expertise with data processing technologies and query authoring (SQL).
• Deep knowledge deploying and managing open source applications internally. Experience with Datahub is a plus.
• A strong commitment to code quality, automation, and operational excellence, including CI/CD pipelines and unit/integration tests.
• Knowledge with data technologies that power analytics, including Spark, Kafka, Airflow, EMR, Hive, and Presto, and handling data at petabyte scale is a big plus.
• Experience designing and building robust distributed microservices using technologies such as Docker, Kubernetes. Experience with Terraform, Chef as IaC solutions is a big plus.
• Exceptional written and verbal communication skills, capable of influencing stakeholders and effectively conveying complex technical concepts.
• Excellent ability to collaborate with cross-functional teams, including product managers, engineers, data scientists, and business stakeholders, to align on requirements and drive data-driven decision-making.
• Ability to provide technical leadership and mentorship, guiding teams on best practices and contributing to the development of data engineering strategies.
• Flexibility and innovative thinking to evaluate and incorporate new technologies and methodologies to improve data processes and solutions.
• High expectations for personal and team growth, with a proactive approach to achieving ambitious goals.

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