Senior Software Engineer, Data

CLEAR - Corporate•New York, NY

9h•$225,000 - $300,000•Onsite

About The Position

As a Senior Software Engineer, Data, you will design, build, and operate the next generation of our data platform and products – going beyond ID to power a networked digital identity – while keeping member privacy, security, and reliability at the core.

Requirements

6+ years of data engineering experience with strong Python and SQL skills, including designing and operating scalable batch and/or streaming data pipelines and orchestration (e.g., Dagster, Airflow, Luigi, dbt, Snowflake, Spark, Kafka, Databricks, or similar technologies).
Working with cloud-based application development and data platforms, including AWS services, modern data warehouses (e.g., Snowflake), evaluating different data platform integration tooling (e.g. APIs, CDC, batch ingestion, streaming) and collaboration/integration tools like GitHub, Argo, Jenkins, or equivalent.
Designing and implementing robust data models and transformations in the data warehouse (e.g., dbt, Snowflake) as well as self-service tooling for the other members of the data platform team.
Articulating technical concepts to mixed technical and non-technical audiences, collaborating across teams, mentoring less experienced engineers, and maintaining clarity in ambiguous problem spaces.
Demonstrating curiosity about technology, a bias toward continuous learning, and a strong sense of ownership over architecture, quality, and process improvements in a fast-moving environment.

Responsibilities

Build and operate scalable, reliable data systems and pipelines – from ingestion to modeling to visualization – so Analysts and Engineers can self-service changes in an automated, tested, secure, and high-quality manner.
Develop and maintain end-to-end data products and pipelines (batch and/or streaming) that collect, clean, transform, and model data, and own the infrastructure that powers them to unlock new business use cases and reporting.
Implement and maintain infrastructure-as-code, CI/CD, and shared developer tooling for data products (e.g., Pulumi/Terraform, GitHub, orchestration tools like Dagster/Airflow) to make it easy and safe for teams to build, test, and ship changes across environments.
Improve the security, compliance, and cost posture of the data stack through robust dependency management, IAM and secrets hardening, observability, and performance/cost optimizations.
Partner with product and other stakeholders to uncover requirements, make architectural decisions, and continuously improve our data platform and processes.