Gigs The Senior Data Analyst at Gigs will shape data modeling, sharing, and insight generation across the company. This role involves collaborating cross-functionally to deliver actionable data products like dashboards and data models.
Responsibilities
As a Senior Data Analyst, you’ll be a foundational part of our data team — shaping how data is modelled, shared, and turned into insight across the company. You’ll join a team of 3 spread across data engineering, analytics engineering and insight generation. You will be the second dedicated Data Analyst in the team.
Salary Range: USD 165,000 - USD 190,000
(The final offer depends on your background, skills, and how you perform through the process. We're open to considering outlier candidates, which may result in an adjustment to the scope and compensation)
Collaborate cross-functionally with teams like Finance, Growth, Product, and Operations to understand their needs and ensure data is actionable and impactful. This could mean delivering a dashboard, an insight, or a data model powering automation.
Be a thought partner on how we define core metrics, track business performance, and build trust in data across Gigs.
Empower internal teams by leveling up power users and educating data consumers, through clear communication, thoughtful documentation, stakeholder collaboration, and occasional training sessions.
Build the analytics content that matters, both internally and externally. You'll work with the Product team to identify key customer-facing metrics and play a crucial role in delivering those insights via dashboards and data products.
Not every data point is available yet. We all work end to end. Work with product to define new data points, translate messy source data into clean, reliable models that power dashboards, metrics, and product decisions.
You will work with a modern event-based data platform across Bigquery, Fivetran, dbt, dagster, Lightdash and Hex.
What We Are Looking For
6+ years of experience in analytics with a focus on end to end delivery. Having worked closely with Finance stakeholders, Product in a B2C context (to support customer launches and ongoing implementations) or experience with customer-facing data products are strong plus points.
A strong understanding of data modeling best practices (e.g. dimensional modeling, testing, documentation).
