We are looking for a Backend Engineer with deep data engineering expertise to build and evolve the systems that power product, analytics, experimentation, and AI development.
You will work across production backend services and data infrastructure, taking technical ownership of the full data lifecycle - from event ingestion and real-time processing to orchestration, modeling, quality, and reliable data access. This role combines hands-on backend development with ownership of shared data platform, working closely with backend, frontend, AI, DevOps, analytics, and product teams.
Key Responsibilities:
Build and maintain production-grade Python and Java backend services, APIs, and reactive data-processing pipelines with clear interfaces, resilient failure handling, and comprehensive observability.
Own and evolve batch and streaming data pipelines supporting product analytics, learner insights, experimentation, and AI model training.
Design and maintain trusted data models, shared metrics, and semantic-layer business logic across the analytical data platform.
Define and enforce standards for data contracts, testing, lineage, freshness, and observability.
Develop reliable approaches to schema evolution, backfills, replay, workload distribution, and failure recovery.
Collaborate with DevOps, backend, frontend, AI, analytics, and product teams to deliver reusable data capabilities and maintain clear system boundaries.
Requirements: At least 6 years of production software engineering experience, primarily in backend systems, including meaningful ownership of data-intensive platforms or infrastructure.
Strong proficiency in Python, Java, and SQL, with experience in testing, typing, performance optimization, and production debugging.
Strong backend engineering fundamentals, including service and API design, asynchronous and concurrent processing, failure handling, and operational reliability.
Experience with relational and non-relational databases such as PostgreSQL, Redis, or MongoDB.
Hands-on experience with AWS, Docker, Kubernetes, CI/CD, and production observability.
Deep experience designing and operating ETL/ELT pipelines and batch or streaming data systems.
Hands-on production experience with Apache Flink for distributed stream processing.
Hands-on production experience building reactive data-processing pipelines with RxJava/Project Reactor, including backpressure, scheduling, error handling, testing, and operational debugging.
Experience with event-driven architectures and messaging systems such as Kafka or AWS SQS.
Strong understanding of data modeling, warehouse and lakehouse architecture, schema evolution, and data quality.
Hands-on experience with dbt for data modeling, testing, documentation, lineage, and semantic-layer development.
Hands-on experience with at least one analytical data platform such as Snowflake, Databricks, ClickHouse, Athena, Trino, or Dremio.
Hands-on experience with at least one dataframe library: Pandas, Polars, or Daft.
Experience with workflow orchestration tools such as Airflow or Dagster.
Clear communication skills and a strong ownership mindset, with a track record of leading cross-functional technical initiatives through to production.
This position is open to all candidates.