Data Engineer

Role Overview

We are looking for a Data Engineer to help build, maintain, and scale our data infrastructure. You will work closely with product, finance, growth, and leadership teams to ensure our business has accurate, reliable, and useful data for decision-making.

Key Responsibilities

• Design, build, and maintain reliable data pipelines from multiple sources such as product databases, payment systems, marketing platforms, and third-party tools.
• Develop and manage data models for reporting, analytics, and business intelligence.
• Ensure data accuracy, consistency, and quality across dashboards and reports.
• Build automated workflows for data extraction, transformation, and loading.
• Support subscription, revenue, user, cohort, retention, and product usage analytics.
• Work with business teams to understand reporting needs and translate them into scalable data solutions.
• Maintain and improve the company’s data warehouse.
• Create clean, well-documented datasets for analysts and business users.
• Monitor data pipelines and troubleshoot data issues quickly.
• Implement best practices around data governance, security, and access control.
• Collaborate with engineering teams to improve event tracking and data collection.
• Help define key business metrics such as MRR, ARR, GMV, churn, LTV, CAC, retention, and cohort revenue.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Analytics, Information Systems, or a related field.
• 3+ years of experience in data engineering, analytics engineering, or a similar role.
• Strong SQL skills.
• Experience with data warehouses such as BigQuery, Snowflake, Redshift, or similar.
• Experience building ETL or ELT pipelines.
• Familiarity with tools such as dbt, Airflow, Fivetran, Stitch, or similar.
• Experience working with BI tools such as Looker, Tableau, Power BI, Metabase, or similar.
• Good understanding of data modelling, data quality, and documentation.
• Comfortable working in a fast-paced startup environment.
• Strong problem-solving skills and attention to detail.
• Able to communicate clearly with both technical and non-technical teams.

Good to Have

• Experience in a SaaS, subscription, payments, creator economy, or marketplace business.
• Experience with product analytics tools such as Mixpanel, Amplitude, Segment, or PostHog.
• Experience handling payment, subscription, refund, chargeback, or revenue data.
• Basic Python knowledge for automation and data processing.
• Understanding of SaaS metrics such as MRR, ARR, churn, retention, LTV, CAC, and cohort analysis.
• Experience supporting finance, growth, or revenue analytics.

What Success Looks Like

• Business teams can trust the data they use for decision-making.
• Key dashboards and reports are accurate, automated, and easy to understand.
• Data pipelines run reliably with minimal manual intervention.
• Revenue, subscription, user, and product data are well-structured and properly documented.
• Leadership has clear visibility into company performance metrics.