Senior Data Engineer, Sprout Social

• We’re looking for a data engineer with a strong foundation in data infrastructure and a passion for enabling others to succeed through high-quality data
• 5+ years of professional experience in data engineering or at least 5 years of hands-on experience building, deploying, and maintaining production-grade data infrastructure and pipelines
• Advanced/Expert level in SQL (e.g. MySQL, PostgreSQL) and data modeling; Proven experience modeling across a diversity of business domains
• Proficiency in Python
• Working familiarity with ELT + transformation frameworks (e.g. , dbt) and with orchestrators (e.g. , Airflow, dbt.)
• Experience building internal data products (curated datasets, semantic layers, or reusable modeling frameworks)
• Pragmatic engineering habits: testing, version control, PR reviews, incident management, incident CI/CD, documentation, and observability for data
• Built transformations with dbt (or similar) and managed semantic layers/metrics for BI (Tableau, Hex, Looker)
• Demonstrated experience collaborating with stakeholders from functions such as Product, Analytics, or Finance to gather requirements, define project scope, and deliver data solutions

Desirable

• Sprout Social is looking for a Data Engineer to join our Data Foundations team
• This team builds the internal data infrastructure, pipelines, and products that empower analytics, data science, and business stakeholders across Sprout
• While our software engineers are focused on delivering customer-facing platform features, our data engineers specialize in ensuring data is reliable, well-modeled, and accessible to fuel smarter decisions and internal innovation
• You’ll be helping build the pipelines, curated datasets, and data products that unlock value across the business and extend Sprout’s data-driven culture
• Implement ELT with managed connectors and/or open-source ingestion; codify transformations with frameworks that bring testing, CI/CD, and data contracts to analytics code
• Create robust data ingest from both common and custom sources; design resilient models that serve analytics, experimentation, and operations
• Partner with product and business stakeholders to define clear metrics and a semantic layer
• Implement best practices in schema design, data modeling, and metadata management
• Own and evolve internal data infrastructure for quality, monitoring, and discoverability
• Provide pragmatic analyst and data science support (SQL debugging, orchestration bootstraps, troubleshooting) to unblock teams while the function scales
• Mentor Sprout’s Data Eng team members and help in the hiring process
• Partner with software engineering teams where application data intersects with internal pipelines—ensuring business-critical data is clean, structured, and usable
• Within 1 month, you’ll plant your roots, including:
• Complete Sprout’s New Hire onboarding program and meet peers across Data Foundations, Data Science, Engineering
• Learn the team’s existing data stack, pipelines, and modeling frameworks
• Shadow teammates to understand how internal stakeholders use curated datasets today
• Partner with your manager to scope your first pipeline or modeling task to own
• Within 3-6 months, you’ll start hitting your stride by:
• Delivering your first production-ready pipeline, dataset, or internal data product
• Collaborating with analysts and data scientists on requirements for reusable modeling layers
• Gaining deeper familiarity with Sprout’s data warehouse(s) and orchestration environment—and starting to suggest improvements
• Audit our existing deployment procedures
• Add data sources to our production data warehouse
• Within 12 months, you’ll make this role your own by:
• Taking technical ownership of a set of pipelines or data products relied on by stakeholders across the business
• Identify technical debt and performance bottlenecks within our systems, come up with a plan to improve the code, and get it pushed to production
• Acting as a mentor to newer members of the Data Foundations team while continuing to grow your own expertise
• Helping shape the team’s roadmap and long-term data foundations strategy
• Of course what is outlined above is the ideal timeline, but things may shift based on business needs and other projects and tasks could be added at the discretion of your manager