Staff Agentic Data Engineer – Data Platform

Nucleus Security

. Own the architecture and evolution of large-scale data processing and analytics systems, from ingestion and normalization through transformation, aggregation, and customer-facing analytics .

Posted 11/5/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing large-scale data processing systems, including proficiency in SQL, Spark, and SingleStore/MemSQL. Capable of leading architectural improvements and mentoring teams while effectively communicating complex technical concepts.

Highest-signal resume keywords

Expert-Level SQLSingleStore/MemSQL ExperienceSpark/Flink Data ProcessingData Modeling for Analytical WorkloadsDistributed Job Processing Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingSQL Query OptimizationDistributed Data ProcessingData Pipeline DesignPerformance TuningPython ProgrammingJava ProgrammingTesting Strategies for Data SystemsArchitectural Pattern DesignBatch Processing

Soft Skills

Technical LeadershipEffective CommunicationCollaboration Across TeamsProblem-SolvingMentoring

Tools & Technologies

SingleStore/MemSQLSparkFlinkLinuxCloud InfrastructureAgile/Scrum Methodologies

Industry Keywords

Data ProcessingAnalytics SystemsHigh-Volume WorkloadsPerformance BottlenecksData TransformationObservabilityCybersecurity DatasetsVulnerability Scanning Technologies

Tech Stack

Tools & technologies

CloudCyber SecurityDistributed SystemsJavaLinuxPythonSparkSQL

About the role

Key responsibilities & impact

Own the architecture and evolution of large-scale data processing and analytics systems, from ingestion and normalization through transformation, aggregation, and customer-facing analytics
Design and evolve data models supporting high-volume analytical workloads while balancing query performance, storage efficiency, maintainability, and scalability
Design and optimize complex analytical SQL queries across large datasets, identifying performance bottlenecks and developing scalable solutions
Architect distributed and asynchronous job processing systems for reliable processing of large data volumes across concurrent workloads
Design and evolve Spark, Flink, or similar data-processing workflows for large-scale transformation, enrichment, aggregation, and computation
Drive effective use of SingleStore/MemSQL, including schema design, indexing and partitioning strategies, query optimization, and workload design
Establish architectural patterns for batch processing, data transformation, job orchestration, failure recovery, idempotency, and reprocessing
Diagnose complex performance and scalability issues across databases, distributed processing systems, application services, and infrastructure
Partner with engineering and product teams to translate ambiguous analytical and data-processing requirements into scalable technical architectures
Define technical standards and reusable patterns for data modeling, data processing, and analytical query development
Provide technical leadership across engineering initiatives, influencing architecture and mentoring engineers while remaining hands-on with implementation
Identify architectural limitations and lead improvements to increase platform scalability, reliability, and performance

Requirements

What you’ll need

10+ years of professional software engineering, data engineering, or data platform experience, with significant experience designing and operating production data systems
Deep expertise in relational and analytical data modeling, including designing schemas for large-scale analytical and reporting workloads
Expert-level SQL, with demonstrated experience designing, analyzing, and optimizing complex analytical queries over large datasets
Strong experience with SingleStore/MemSQL or comparable distributed SQL/analytical database technologies
Significant hands-on experience with Spark, Flink or similar technologies, including designing and optimizing distributed data-processing workloads
Strong understanding of distributed data processing, including partitioning, parallelism, data skew, resource utilization, and performance optimization
Experience designing high-throughput job processing systems, including scheduling, concurrency management, retries, failure recovery, idempotency, and observability
Experience designing data pipelines that support batch ingestion, transformation, enrichment, aggregation, and downstream analytics
Strong understanding of database performance characteristics, including indexing, partitioning, query execution plans, data distribution, and memory/compute trade-offs
Experience identifying and resolving performance bottlenecks across application, database, and data-processing layers
Proficiency in Python, Java or another production programming language used to build data-processing and distributed systems
Strong understanding of testing strategies for data-intensive systems, including unit, integration, data-quality, and end-to-end testing
Proven ability to independently own technically ambiguous problems and drive them from investigation through production implementation
Demonstrated ability to influence architecture and technical direction across multiple teams without relying solely on organizational authority
Ability to communicate complex data architecture and performance trade-offs clearly to engineers, product stakeholders, and technical leadership
Ability to collaborate effectively with distributed teams across multiple time zones
Preferred: Deep production experience with SingleStore/MemSQL, particularly with large-scale analytical workloads
Preferred: Advanced experience with pipeline performance tuning, partitioning strategies, memory management, and optimization of large distributed workloads
Preferred: Experience designing platforms that support a combination of high-volume ingestion and low-latency analytical queries
Preferred: Experience building or operating distributed job execution and orchestration systems
Preferred: Experience with large-scale cybersecurity, vulnerability, asset, telemetry, or event datasets
Preferred: Experience working with vulnerability scanning technologies across the security stack, including SCA, SAST, DAST, IAST, vulnerability scanning, and container security
Preferred: Experience designing systems for incremental processing, backfills, replay/reprocessing, deduplication, and late-arriving data
Preferred: Experience with data observability and operational monitoring for large-scale processing workloads
Preferred: Experience integrating AI/LLM-driven capabilities with data platforms, including preparing and processing large datasets for AI-assisted analytical workflows
Preferred: Experience operating data-intensive applications on Linux and cloud infrastructure
Preferred: Familiarity with Agile/Scrum methodologies in a professional engineering environment

Benefits

Comp & perks

Flexible PTO
401k company contribution (not a match, a contribution)
Generous education and training budget
100% company-paid healthcare
Fully remote work