Were looking for a highly skilled and motivated Data Engineer to join the Resolve (formerly DevOcean) team .
In this role, youll be responsible for designing, building, and optimizing the data infrastructure that powers our SaaS platform.
Youll play a key role in shaping a cost-efficient and scalable data architecture while building robust data pipelines that serve analytics, search, and reporting needs across the organization.
Youll work closely with our backend, product, and analytics teams to ensure our data layer remains fast, reliable, and future-proof. This is an opportunity to influence the evolution of our data strategy and help scale a cybersecurity platform that processes millions of findings across complex customer environments
Roles and Responsibilities:
Design, implement, and maintain data pipelines to support ingestion, transformation, and analytics workloads.
Collaborate with engineers to optimize MongoDB data models and identify opportunities for offloading workloads to analytical stores (ClickHouse, DuckDB, etc.).
Build scalable ETL/ELT workflows to consolidate and enrich data from multiple sources.
Develop data services and APIs that enable efficient querying and aggregation across large multi-tenant datasets.
Partner with backend and product teams to define data retention, indexing, and partitioning strategies to reduce cost and improve performance.
Ensure data quality, consistency, and observability through validation, monitoring, and automated testing.
Contribute to architectural discussions and help define the long-term data platform vision.
In this role, youll be responsible for designing, building, and optimizing the data infrastructure that powers our SaaS platform.
Youll play a key role in shaping a cost-efficient and scalable data architecture while building robust data pipelines that serve analytics, search, and reporting needs across the organization.
Youll work closely with our backend, product, and analytics teams to ensure our data layer remains fast, reliable, and future-proof. This is an opportunity to influence the evolution of our data strategy and help scale a cybersecurity platform that processes millions of findings across complex customer environments
Roles and Responsibilities:
Design, implement, and maintain data pipelines to support ingestion, transformation, and analytics workloads.
Collaborate with engineers to optimize MongoDB data models and identify opportunities for offloading workloads to analytical stores (ClickHouse, DuckDB, etc.).
Build scalable ETL/ELT workflows to consolidate and enrich data from multiple sources.
Develop data services and APIs that enable efficient querying and aggregation across large multi-tenant datasets.
Partner with backend and product teams to define data retention, indexing, and partitioning strategies to reduce cost and improve performance.
Ensure data quality, consistency, and observability through validation, monitoring, and automated testing.
Contribute to architectural discussions and help define the long-term data platform vision.
Requirements:
8+ years of experience as a Data Engineer or Backend Engineer working in a SaaS or data-intensive environment.
Strong proficiency in Python and experience with data processing frameworks (e.g., Pandas, PySpark, Airflow, or equivalent).
Deep understanding of data modeling and query optimization in NoSQL and SQL databases (MongoDB, PostgreSQL, etc.).
Hands-on experience building ETL/ELT pipelines and integrating multiple data sources.
Familiarity with OTF technologies and analytical databases such as ClickHouse, DuckDB and their role in cost-efficient analytics.
Experience working in cloud environments (AWS preferred) and using native data services (e.g., Lambda, S3, Glue, Athena).
Strong understanding of data performance, storage optimization, and scalability best practices.
Excellent problem-solving skills and a proactive approach to performance and cost optimization.
Strong collaboration and communication abilities within cross-functional teams.
Passion for continuous learning and exploring modern data architectures.
Nice to Have:
Experience with streaming or CDC pipelines (e.g., Kafka, Debezium).
Familiarity with cloud security best practices and data governance.
Exposure to multi-tenant SaaS architectures and large-scale telemetry data.
8+ years of experience as a Data Engineer or Backend Engineer working in a SaaS or data-intensive environment.
Strong proficiency in Python and experience with data processing frameworks (e.g., Pandas, PySpark, Airflow, or equivalent).
Deep understanding of data modeling and query optimization in NoSQL and SQL databases (MongoDB, PostgreSQL, etc.).
Hands-on experience building ETL/ELT pipelines and integrating multiple data sources.
Familiarity with OTF technologies and analytical databases such as ClickHouse, DuckDB and their role in cost-efficient analytics.
Experience working in cloud environments (AWS preferred) and using native data services (e.g., Lambda, S3, Glue, Athena).
Strong understanding of data performance, storage optimization, and scalability best practices.
Excellent problem-solving skills and a proactive approach to performance and cost optimization.
Strong collaboration and communication abilities within cross-functional teams.
Passion for continuous learning and exploring modern data architectures.
Nice to Have:
Experience with streaming or CDC pipelines (e.g., Kafka, Debezium).
Familiarity with cloud security best practices and data governance.
Exposure to multi-tenant SaaS architectures and large-scale telemetry data.
This position is open to all candidates.
