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About Ocean
Ocean is an email security company. Our research & detection team is the analytical core behind the product: we build AI systems that catch the attacks that slip past everyone else - phishing, social engineering, BEC, impersonation, AiTM/credential phishing, and AI-generated campaigns - and we back our customers' security teams with deep investigation and research.
We’re looking for a Senior Data Engineer to help design, build, and scale the data platform behind Ocean’s product. You’ll own high-scale data pipelines, shape the architecture of our batch and streaming systems, and work closely with backend, data science, and product teams to turn raw data into reliable, fast, and trustworthy information.
This is a hands-on senior role for someone who has seen several data platforms evolve, knows what works at scale (and what breaks), and wants real influence over technical direction.
What you'll do
Design, build, and maintain high-scale data pipelines, both batch and real-time streaming, that are reliable, observable, and cost-efficient
Shape the architecture of our data platform, including ingestion, storage, modeling, and serving layers, and make pragmatic decisions about tools and trade-offs
Build backend services and APIs that produce, process, or expose data, working as a peer to our backend engineers
Optimize performance and cost across large datasets, from query tuning to storage layout and partitioning strategies
Set standards for data quality, schema evolution, testing, and monitoring so other teams can trust the data
Partner with product, data science, and engineering stakeholders to understand needs and translate them into scalable data solutions
Mentor other engineers and contribute to design reviews, code reviews, and technical discussions across the team
Requirements
6+ years of professional experience in Data Engineering, Backend Engineering, or a combination of both, including at least 2 years of hands-on experience in Data Engineering
Proven experience building and operating data systems at high scale (high throughput, large volumes, low-latency requirements)
Hands-on experience with both batch and streaming ETL/ELT pipelines, using technologies such as Kafka, Flink, Spark Streaming, Airflow, dbt, or similar
Broad exposure to different data platforms and architectures (for example, data lakes, lakehouses, data warehouses, OLAP stores) and the judgment to know when each fits
Strong backend engineering skills in at least one language such as Python, Java, Scala, or Go, including writing production-grade services, not only scripts
Strong SQL and data modeling skills
Experience with cloud infrastructure (GCP, AWS, or Azure) and containerized environments such as Kubernetes
Ownership mindset: you care about reliability, monitoring, and the long-term health of the systems you build
Clear communication and the ability to drive technical decisions with stakeholders
Nice to have
Experience with OLAP databases such as ClickHouse or Snowflake, including schema design, materialized views, and query optimization for analytical workloads
Experience with Apache Spark for large-scale data processing
Experience with lakehouse technologies such as Databricks, Delta Lake, or Iceberg
Experience mentoring engineers or leading technical projects end-to-end