Senior Analytics Engineer

Sprout Social is looking to hire a Senior Analytics Engineer to the Data team.

Why join Sprout’s Data Science and Engineering team?

At Sprout, we've only scratched the surface of what's possible with our data. There’s no shortage of opportunities to make a big impact on the business through the development of data products such as new machine learning systems, KPIs and dashboards, or new data integrations and data sources. Because Data Science, Data Engineering and Business Analytics all work under the same team here, there aren't the standard organizational roadblocks teams sometimes face to do their work. Our work impacts and touches all corners of the business, and we enable Sprout’s data culture through support, teaching and mentoring across teams. Come join us as we seek to act as both engineers and advisors, building functionality within the Sprout ecosystem and partnering with our colleagues across the organization to enable a more data-informed culture.

What you’ll do

Embedded within the Data Science and Engineering team, you will work with our business partners, analysts, and data engineers to ensure that Sprout’s business gets access to timely and accurate data to drive decision making and reporting.
Relying on your experience with data warehousing and writing SQL, you will gather business requirements and translate them into well-documented, tested and accurate data artifacts.
Leveraging your experience with dimensional data modeling, you will help refine and define the path towards a more mature data warehousing capability to empower Sprout’s business and enable ever-growing size and uses of our data.
You will help Sprout’s broader data community to leverage the power of our data by mentoring analysts about SQL, DBT, and data modeling, attending office hours, presenting in our Data Analysis and Visualization guild meetings, and help create and maintain content to educate Sprout’s broader analytics community.

What you’ll bring

We’re looking for a collaborative, organized, detail-oriented, motivated, and inquisitive learner to help take our data warehousing game to the next level. If you confidently write SQL, translate business requests into data artifacts, are comfortable communicating with both engineers and business stakeholders, are familiar with dimensional data modeling, and are passionate about learning, we’d love to talk with you!

The minimum qualifications for this role include:

4+ years in the Data space as an analyst, engineer, or equivalent.
3+ years experience designing, implementing, operating, testing, and extending data models.
3+ years of experience working with both technical and business stakeholders to achieve consensus in defining data warehousing requirements.
Familiarity with Dimensional modeling concepts for data warehousing.

Preferred qualifications for this role include:

Experience with cloud data warehousing technologies such as Redshift and experience tuning SQL to leverage the underlying database.
Demonstrated ability to analyze, reverse engineer, and optimize existing SQL.
2+ years managing projects, including helping define deliverables, breaking down tasks and milestones, and communicating expectations to stakeholders.
Experience with our tech stack: Git/Github, dbt, Redshift, Python, Jupyter Notebooks, Great Expectations, & Amundsen (or equivalent metadata management tool).
Experience mentoring or coaching others around SQL and data modeling.
2+ years experience building reports and dashboards in a data visualization tool and/or leveraging Python to analyze and manipulate data.
Experience working with CRM data, such as Salesforce or Hubspot.

How you’ll grow

Within 1 month, you’ll plant your roots, including:

Completing Sprout’s New Hire training program alongside other new Sprout team members.
Learning about Airflow, DBT, and the rest of our data stack supporting our ETL and ELT processes.
Pairing with our data engineers and analytics engineers to understand the systems we have for publishing data.
Becoming familiar with data sources available in our data lake.

Within 3 months, you’ll start hitting your stride by:

Leveraging Data Build Tool (DBT) and internal tools to add data sources to our production data warehouse.
Writing tests against existing data sources using Great Expectations.
Shadowing support rotations and office hours to learn how to manage our systems and help educate and support our partners around the business.
Beginning documenting information about our warehouse data sources including lineage and data definitions in our metadata management system.
Working with data engineers and our analytics manager to help re-architect critical portions of our data warehouse.
Establishing relationships with business stakeholders and analysts to understand their domain and leverage that knowledge to create and/or update existing data sources for their reporting and analysis needs.

Within 6 months, you’ll be making a clear impact through:

Executing on reorganizing parts of our data warehouse using dimensional data modeling techniques.
Serving as on-call for support rotations and participating in office hours.
Creating content that helps educate end-users about our data resources and how to use them.
Leveraging tests you and others have written to monitor data flowing through our systems.
Continuing to document and develop tests to monitor and alert on critical data sources in our warehouse.
Forming a career growth plan with your manager and work towards it.

Within 12 months, you’ll make this role your own by:

Expanding your skills by learning from data engineers, analysts, and data scientists around Sprout.
Owning one or more stakeholder relationships.
Serving as a subject matter expert and data model spokesperson, demonstrated by the ability to address questions quickly and accurately.
Taking ownership over the data flowing through our event-tracking systems by helping establish and refine governance practices and coordinating with engineering.
Finding new opportunities for improving data integrity, visibility and accessibility.
Guiding and educating other data users around Sprout to self-serve their data needs.
Surprise us! Use your unique ideas and abilities to change your team in beneficial ways that we haven’t even considered yet.

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.

The base pay range for this role is $110,000 - $125,000 USD annually. Individual base pay is based on various factors, including relevant experience and skills, the responsibility of the role, and job duties/requirements.

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