As a Senior Engineer for our Data Platform team, you will be responsible for maintaining and continuously improving our data platform. We are dedicated to designing, implementing and maintaining Hinge’s data infrastructure, while playing a critical role in selecting and implementing technology that will help Hinge to make data-driven decisions, build a best-in-class product, reporting & analytics, and enable ML/AI use cases.
You will work cross-functionally with multiple engineering teams, providing leadership and advice while serving as a crucial partner to various teams and stakeholders, including Business Intelligence, Product Data Engineering, Data Science, MLE & AI, and more.
You will be making a real impact on the data platform team and will be key to the success of Hinge. Your work will enable the organization to make data-driven decisions and drive improvements, which will affect the love lives of tons of people.
Responsibilities:
Design, develop, and maintain core services within Hinge’s data platform.
Build and maintain our lakehouse that provides clean, accurate, and robust data sets to be leveraged for reporting, analytics, and machine learning initiatives.
Responsible for maintaining and enhancing data pipelines and database management frameworks, which include tasks such as automation, monitoring, alerting, governance, and cost optimization.
Work on observability systems that enables holistic system and data quality monitoring
Solicit and incorporate internal user feedback to ensure productivity tooling meets the needs of our developers.
Work with our data teams to ensure data is flowing accurately through data creation to our presentation layers.
Continue to learn more about the Data Engineering discipline, utilize that knowledge in your deliverables, and identify opportunities to enhance our pipelines.
- Participate in our on-call rotation to ensure system reliability, promptly addressing issues to ensure smooth operations.
What We're Looking For:
4+ years of professional/industry experience.
Experience in building, automating, and enhancing tools and frameworks for the data platform to streamline pipeline development.
Experience modeling data sets for different types of sources and business processes.
Strong communication skills (written/verbal).
Exposure to ML Ops or other data engineering workloads.
Effective analytical, troubleshooting, and problem-solving skills.
A degree in computer science, engineering, or a related field.
Programming: Python, Go, Scala, Java
Cloud platform: The ability to utilize cloud environments such as GCP, AWS, or Azure.
DevOps tooling: Kubernetes, Docker, Terraform, CircleCi, GitHub Actions
Streaming: Kafka, PubSub, Dataflow
Databases and Big Data Tooling: Redshift, BigQuery, Databricks, dbt
BI platform: Looker
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