Job Description:

Role: Security Data Engineer

Experience: 7 to 12 Years

Notice period: Immediate to 15 days

Location: Hyderabad

Required Technical Skill Set: Security Data Engineering, Security Knowledge Graph Integration (Neo4j / Cypher / Graph Ingestion), ML Engineering / MLOps, Enterprise ITSM & Telemetry Integrations (ServiceNow CMDB / APIs, Dynatrace APM / OpenTelemetry), Cloud Data Pipelines (GCP BigQuery / Dataflow / Azure Synapse / Databricks), Big Data Processing (Apache Spark / Kafka / PySpark), Python / SQL, CI/CD & DataOps

Experience Range: 7 12 Years overall
Technical experience (with 3+ years in data engineering, graph data pipelines, or security/telemetry integration)

Must-Have (Candidates must demonstrate depth in core data engineering and a combination/blend of the following specialisms)
• Enterprise Data Engineering & Pipelines: Proven experience architecting and building high-throughput batch and real-time streaming data pipelines using Python, PySpark, Apache Spark, Kafka, and Cloud Data Warehouses (GCP BigQuery / Dataproc, Azure Synapse / Databricks, or AWS).
• Security Knowledge Graph Ingestion & Modeling: Hands-on experience building ingestion contracts, graph ETLs, and entity resolution pipelines to populate Graph Databases (Neo4j, Amazon Neptune, Azure Cosmos DB / Gremlin) using Cypher or Gremlin.
• ServiceNow & Dynatrace Integration Engineering: Deep practical experience integrating with enterprise ITSM and APM platforms:



extracting configuration items (CIs), relationships, and change data from ServiceNow CMDB / REST APIs, and ingesting telemetry, metrics, spans, and problem events from Dynatrace / OpenTelemetry.
• ML Engineering & MLOps Pipelines: Experience operationalising data pipelines for machine learning models, managing feature pipelines, embeddings storage, model registries, and data quality validation frameworks (e.g., Great Expectations, MLflow, Vertex AI / Azure ML pipelines).
• Entity Resolution & Data Governance: Expertise in data reconciliation, deduplication, identity matching, and lineage tracking across disparate security assets, identities, vulnerabilities, and CMDB records.
• Software Engineering & DataOps: Strong proficiency in Python, advanced SQL, automated testing for data pipelines, and CI/CD automation (GitHub Actions, GitLab CI, Azure DevOps).

Good-to-Have
• Experience with Graph-as-a-Service (GaaS) or Context-as-a-Service architectures supporting LLM agents and GraphRAG pipelines.
• Familiarity with Cyber Security domains (SIEM telemetry, vulnerability management, cloud asset posture/CSPM, MITRE ATT&CK; mapping).
• Knowledge of event-driven architectures and message queuing (Kafka, Pub/Sub, Event Hubs).
• Certifications: GCP Professional Data Engineer, Azure Data Engineer Associate, Neo4j Certified Skilled, Databricks Certified Data Engineer, or equivalent.

📌 Security Data Engineer (Hyderabad)
🏢 Cerebra
📍 Hyderabad