Architect, build, and own real-time and batch data pipelines that power attribution decisions and marketing insights.
Deliver high-quality, reliable datasets for customer dashboards, fraud systems, and internal analytics use cases.
Optimize ingestion and aggregation performance using tools like Flink, Spark, Kafka, and Druid.
Partner with the Data Platform team to make infrastructure-level decisions that impact performance, latency, and cost.
Own schema design, versioning, and deployment of datasets across Iceberg, S3, and other analytical data stores.
Build and maintain robust monitoring, alerting, and self-healing mechanisms to ensure high system availability.
Collaborate cross-functionally with Product, Customer Success, and Data Science to identify and deliver new data capabilities.

Requirements

5+ years of software engineering experience, ideally in data engineering or large-scale backend systems.
Proficiency in SQL and at least one backend language (Java, or Python).
Understanding of distributed systems, data modeling, and real-time data processing.
Hands-on experience with AWS cloud tools and big data platforms such as Kafka, Flink, Spark, Airflow, dbt, Druid.
A solid grasp of data warehousing principles and familiarity with columnar storage formats (Parquet, Avro).
Curiosity and drive to work with event-driven data systems that operate at massive scale.
Strong communication skills and a desire to collaborate across time zones and teams.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data pipelinesSQLJavaPythondata modelingreal-time data processingdata warehousingcolumnar storage formatsschema designversioning

Soft Skills

communicationcollaborationcuriositydrive