We are looking for an Experienced Data Engineer to join our marketing team and take end-to-end ownership of our data platform and production data pipelines. In this role, you will be responsible for building robust, scalable, and observable data systems that power analytics, reporting, and downstream business use cases. You will work deeply hands-on with data infrastructure, modeling, and orchestration, and act as a key technical partner to Marketing, Sales Product and Business and Finance teams.
This role suits someone who enjoys working close to the metal, designing systems that scale, and solving ambiguous data problems in a dynamic startup environment. You will play a critical role in shaping how data flows through the company, setting engineering standards, and ensuring data is trustworthy, performant, and ready for growth.
What You'll Do:
Design, build, and maintain scalable, reliable data pipelines and data warehouse architectures to support analytics and business intelligence needs.
Own the end-to-end ETL/ELT processes – ingesting data from internal and external sources, transforming it, and making it analytics-ready.
Model and optimize data structures (fact tables, dimensions, semantic layers) to support performant querying and reporting.
Ensure high standards of data quality, integrity, observability, and reliability across all data assets.
Partner closely with Analytics, Product, Marketing, and Finance teams to understand data requirements and deliver robust data solutions.
Implement monitoring, alerting, and testing frameworks to proactively identify data issues.
Optimize warehouse performance and cost efficiency (query optimization, partitioning, clustering, etc.).
Identify gaps in data collection and work with engineering teams to improve instrumentation and data availability.
Support experimentation and analytics use cases by enabling clean, trustworthy datasets for A/B testing and analysis.
Document data models, pipelines, and best practices to support scale and knowledge sharing.
This role suits someone who enjoys working close to the metal, designing systems that scale, and solving ambiguous data problems in a dynamic startup environment. You will play a critical role in shaping how data flows through the company, setting engineering standards, and ensuring data is trustworthy, performant, and ready for growth.
What You'll Do:
Design, build, and maintain scalable, reliable data pipelines and data warehouse architectures to support analytics and business intelligence needs.
Own the end-to-end ETL/ELT processes – ingesting data from internal and external sources, transforming it, and making it analytics-ready.
Model and optimize data structures (fact tables, dimensions, semantic layers) to support performant querying and reporting.
Ensure high standards of data quality, integrity, observability, and reliability across all data assets.
Partner closely with Analytics, Product, Marketing, and Finance teams to understand data requirements and deliver robust data solutions.
Implement monitoring, alerting, and testing frameworks to proactively identify data issues.
Optimize warehouse performance and cost efficiency (query optimization, partitioning, clustering, etc.).
Identify gaps in data collection and work with engineering teams to improve instrumentation and data availability.
Support experimentation and analytics use cases by enabling clean, trustworthy datasets for A/B testing and analysis.
Document data models, pipelines, and best practices to support scale and knowledge sharing.
Requirements:
Bachelors or Masters degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
3-5 years of hands-on experience as a Data Engineer, preferably in a SaaS or technology-driven environment.
Strong experience designing and maintaining data warehouses (e.g., Snowflake, BigQuery, Redshift).
Proven expertise with ETL/ELT tools and frameworks (e.g., Airflow, dbt, Talend, SSIS, Informatica, or similar).
Advanced SQL skills and solid proficiency in Python (or similar languages) for data processing and orchestration.
Strong understanding of data modeling, warehousing best practices, and analytics engineering concepts.
Experience integrating data from business systems such as Salesforce, HubSpot, or other SaaS platforms.
Familiarity with SaaS metrics and business concepts (ARR, churn, LTV, CAC) – from a data modeling perspective.
Experience supporting BI tools and analytics consumers (Tableau, Looker, Power BI, etc.).
Strong problem-solving skills, attention to detail, and a passion for building reliable data foundations.
Excellent communication skills and the ability to collaborate across technical and non-technical teams.
Bachelors or Masters degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
3-5 years of hands-on experience as a Data Engineer, preferably in a SaaS or technology-driven environment.
Strong experience designing and maintaining data warehouses (e.g., Snowflake, BigQuery, Redshift).
Proven expertise with ETL/ELT tools and frameworks (e.g., Airflow, dbt, Talend, SSIS, Informatica, or similar).
Advanced SQL skills and solid proficiency in Python (or similar languages) for data processing and orchestration.
Strong understanding of data modeling, warehousing best practices, and analytics engineering concepts.
Experience integrating data from business systems such as Salesforce, HubSpot, or other SaaS platforms.
Familiarity with SaaS metrics and business concepts (ARR, churn, LTV, CAC) – from a data modeling perspective.
Experience supporting BI tools and analytics consumers (Tableau, Looker, Power BI, etc.).
Strong problem-solving skills, attention to detail, and a passion for building reliable data foundations.
Excellent communication skills and the ability to collaborate across technical and non-technical teams.
This position is open to all candidates.
