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Tier-1 brand, metro location, common Data Engineer title and broad skill requirements increase candidate density.
Core data engineering skills are transferable, but Salesforce/marketing and finance domain experience favors domain-specific backgrounds.
Explicit 8+ years and required cross-functional data experience make shortlisting highly strict.
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Design, implement and maintain data pipelines and a Data Warehouse model supporting Sales, Customer Success, Marketing, and Finance analytics at Slack within Salesforce.
Partner cross-functionally with business domain experts, data analysts, and engineers to build scalable data foundations, ensuring data quality and compliance with governance policies.
Own documentation of data pipelines, data lineage, and promote best practices across data governance, security, privacy, quality, and retention.
Minimum 8 years experience in data management including data integration, modeling, optimization, or related data engineering.
At least 4 years experience collaborating with business stakeholders in Sales or Finance on multi-departmental data management and analytics initiatives.
Strong expertise with dimensional modeling, data warehouse scaling/optimization, ETL pipeline development, and experience with relational and big data systems.
Work Experience Required: 8+ years in relevant data engineering roles as explicitly mentioned in the JD. Notice Period: Not explicitly mentioned in the JD.
Experienced senior-level data engineer comfortable working across pipeline ETL, data modeling, and complex SQL development in fast-paced technical environments.
Strong collaborator skilled in working cross-functionally with analytics, business stakeholders, and engineering teams to translate business strategy into trusted data foundations.
Technically proficient with data warehouse technologies (Snowflake, Redshift), big data platforms (Hadoop, Hive, Spark), Airflow, and Python programming.