Senior Data Engineer (Enterprise, Slack)

Requirements 5+ years of experience in data architecture, data modeling, master data management, or metadata management Hands‑on expertise with relational modeling approaches — including columnar, star schema, snowflake, and dimensional modeling Proven track record optimizing schemas, tuning SQL, and scaling ETL pipelines in OLAP and data warehouse environments Experience with Airflow or a comparable workflow orchestration platform for data pipeline management Strong proficiency in Python and SQL Familiarity with data governance frameworks, SDLC, and Agile methodologies Excellent written and verbal communication skills with the ability to partner effectively across technical and business teams U.S. citizen or lawful permanent resident, willing to undergo a background check for GovSlack authorization

• Desirable) Hands‑on experience with Spark SQL, AWS (S3, EMR), Apache Pinot, or Hadoop/Big Data ecosystems
• Desirable) Experience with Flink or other real‑time stream processing frameworks
• Desirable) Proficiency in Scala
• Desirable) Familiarity with cloud platforms (AWS, GCP, or Azure)
• Desirable) Experience with NoSQL data stores

What the job involves As a Data Engineer on the Enterprise team, you'll partner cross-functionally with business stakeholders, analytics teams, and backend engineers to design, build, and scale both batch and real‑time data pipelines You'll contribute directly to initiatives that support key decision‑makers within our enterprise customer base — helping them understand adoption, engagement, and the health of their Slack deployments You'll also be a core contributor to GovSlack, our cross‑team initiative to establish feature parity for U.S. government agencies and highly regulated organizations This is high‑visibility, high‑impact work that matters beyond the product — it supports national infrastructure We’re looking for passionate, detail‑oriented engineers who are excited about building a rock‑solid data foundation and making a real impact at one of the world’s most widely used collaboration platforms Translate complex business requirements into scalable, reusable data models that are easy to understand and adopted across subject areas Design, implement, and maintain data pipelines that deliver high‑quality data under defined SLAs Partner with product, analytics, and engineering teams to build trusted, well‑documented foundational datasets aligned with business strategy Champion data strategy across multiple teams and use cases, driving consistency and reliability at scale Expand access to core company metrics through strong technical and process foundations Identify, document, and promote data engineering best practices across Slack #J-18808-Ljbffr Salesforce