Senior Data Engineer

Description

Senior Data Engineer — Marketing Operations

About The Role

As a Senior Data Engineer on the Marketing Operations team, you'll work cross-functionally with business domain experts, analytics, and engineering teams to design and implement our Data Warehouse model. You'll partner with the Business Analytics Data Engineering function to build a data foundation and processes that scale with company growth — architecting, designing, and implementing data pipelines that deliver insights from Product and Corporate Systems to key partners, data scientists, and decision-makers across Sales, Customer Success, Marketing, and Finance at Slack.

This role suits problem solvers who anticipate issues before they surface and look beyond immediate fixes. We're looking for self-starters who take pride in their craft, are detail- and quality-oriented, and are passionate about making a real impact at Slack.

Responsibilities

• Design, implement, and build pipelines that deliver data with measurable quality under SLA.
• Assemble complex, large data sets that meet functional requirements.
• Partner with data architects, domain experts, data analysts, and other teams to build trusted, well-understood foundations aligned with business strategy that enable self-service.
• Champion the overall strategy for data governance, security, privacy, quality, and retention in line with business policies.
• Own and document data pipelines and data lineage.
• Identify, document, and promote best practices.
• Support and maintain the analytics tech ecosystem (data warehouse, ETL, and BI tools).
  
  

• 8+ years of experience in data management — data integration, modeling, optimization, data quality, or related data engineering areas.
• 4+ years working in cross-functional teams, collaborating with business stakeholders in Sales or Finance on departmental/multi-departmental data management and analytics initiatives involving product and business system data.
• Solid experience working with Sales, Finance, and product usage data.
• Expertise in dimensional modeling, data warehouse scaling/optimization, performance tuning, and ETL pipelines.
• Deep understanding of both relational and big data systems.
• Strong problem-solving and interpersonal skills, with the ability to make sound, complex decisions in a fast-paced technical environment.
• Ability to work across multiple areas: pipeline ETL, data modeling & design, and complex SQL.
• Excellent written and verbal communication, able to collaborate with both technical and business partners.
• Strong grasp of trade-offs and the ability to navigate between big-picture strategy and implementation detail.
  
  

• Prior experience with Airflow.
• Hands-on experience with data warehouse technologies (Snowflake, Redshift) and big data technologies (Hadoop, Hive, Spark).
• Proficiency in Python or similar programming languages.
• Passion for data technologies broadly — SQL, NoSQL, MPP databases, etc.