RemoteMust work during US business hours; preference for candidates in a US time zone.
Data & AnalyticsData Engineering
About the job
Requirements
At least 5 years of software engineering experience, including at least 3 years focused on data infrastructure or platform engineering.
Production pipeline experience in a modern cloud warehouse such as BigQuery, Snowflake, or Databricks, using dbt, SQL, or Spark and an orchestrator such as Dagster or Airflow.
Expertise with SQL and at least one general-purpose programming language, preferably Python, for building pipelines, custom integrations, automation scripts, and occasional web services.
Ability to independently navigate complex, unfamiliar systems to identify and fix difficult problems.
Ability to communicate proactively, consistently, and effectively with cross-functional teammates and stakeholders.
Understanding of the testability of data systems.
Ability to use artificial intelligence tools and automation pragmatically to improve work.
Responsibilities
Build, orchestrate, and maintain data pipelines on a Google Cloud Platform stack using BigQuery, dbt, PySpark, Parquet, and Dagster.
Develop a deep understanding of the partner's core data models to collaborate effectively with Data Science and Data Operations.
Partner with Data Science and Data Operations teams to maintain datasets that are trusted, well-understood, and enable self-service.
Work with cross-functional stakeholders to derive and define requirements.
Document, simplify, and explain complex problems to different types of audiences.
Establish best practices for developing specialized datasets for analytics and modeling.
Ensure the accuracy and scalability of Subscriber Metrics.
Own the reliability of data delivery and serve as the subject-matter expert for the delivery pipeline.
Desired Qualifications
Experience designing and implementing backend web services.
Experience managing cloud resources on Google Cloud Platform or Amazon Web Services using infrastructure-as-code tools such as Terraform, Pulumi, or CloudFormation.
Experience designing maintainable systems to solve challenging customer problems.
Industry experience with receipt, transaction, or viewership data.
Experience with data architecture and governance.
About DevSavant DevSavant is an operating partner for startups and growth-stage companies, helping them turn ambition into execution. We support founders and leadership teams with product engineering and global staffing, from early prototypes and MVPs to scaling high-performing teams. Our vetted talent across LATAM and Asia embeds directly into client teams, operating as true extensions rather than external vendors. With over 8 years working in venture-backed ecosystems, DevSavant is trusted to accelerate delivery, scale teams efficiently, and support companies as they reach their next milestone.
Role Overview We are looking for a Senior Data Engineer to join our team to ensure the accuracy and scalability of Subscriber Metrics, one of this partner's core data products. In this role, you will partner with our Data Operations and Data Science teams to tackle complex data engineering problems and integrate data models. You will own the reliability of our data delivery and serve as the subject matter expert for our delivery pipeline. The incoming Senior Data Engineer will join a collaborative team that requires a technical, driven team member who brings a solid programming foundation, cloud platform experience, and a passion for automation.
You will report to the VP, Engineering. This partner is a remote-first company, and we are looking for candidates who can work during US business hours. Preferably, you should be based in a US time zone (Eastern, Central, Mountain, or Pacific).
What You’ll Do
Build, orchestrate, and maintain data pipelines on our GCP stack, using BigQuery and dbt for modeling, PySpark and Parquet for large-scale processing, and Dagster for orchestration.
Develop a deep understanding of this partner's core data models in order to collaborate effectively with Data Science and Data Ops.
Partner with the Data Science and Data Ops teams to maintain datasets that are trusted, well-understood, and enable self-service.
Work with a wide range of cross-functional stakeholders to derive and define requirements.
Document, simplify, and explain complex problems to different types of audiences.
Establish best practices for the development of specialized datasets for analytics and modeling.
Who You Are
5+ years in software engineering, with at least 3 years focused specifically on data infrastructure or platform engineering.
You've built production pipelines in a modern cloud warehouse (BigQuery, Snowflake, or Databricks) with dbt, SQL, or Spark and an orchestrator like Dagster or Airflow.
You have expertise with SQL and at least one general-purpose programming language, preferably Python, for building pipelines, custom integrations, automation scripts, and occasional web services.
You independently navigate complex, unfamiliar systems to identify and fix difficult problems.
You proactively, consistently, and effectively communicate with cross-functional teammates and stakeholders.
You care about and understand the testability of data systems.
You use AI tools and automation to genuinely improve your work, and you're pragmatic rather than reliant on them.
Bonus Qualifications
You design and implement backend web services.
You manage cloud resources (GCP or AWS) using infrastructure as code tools like Terraform, Pulumi, or CloudFormation.
You design maintainable systems to solve challenging customer problems.
You bring industry experience with receipt, transaction, or viewership data.
You have experience with data architecture and governance.