What you will be responsible for

As a Staff Data Platform Engineer, Cyber Data Science, you will:

• Collaborate across a variety of teams to enable our Data Platform and MLOps needs and design, implement, and maintain a secure and scalable infrastructure platform spanning across AWS/Azure and our Data Center
• Use Infrastructure as Code and containerization to create immutable reproducible deployments and establish best practices to scale that Infrastructure as Code (IaC) project in a maintainable way
• Own and ensure that internal and external SLA’s meet and exceed expectations, System centric KPIs are continuously monitored
• Create tools for automating deployment, monitoring, alerting and operations of the overall platform and establish best practices for CI/CD environments and methodologies such as GitOps
• Analyze our AWS/Azure Resource usage to optimize for a balance of performance vs cost
• Work on our data lake, data warehouse, and stream processing systems to create a unified query engine, multi-model databases, analytics extracts and reports, as well as dashboard and visualizations
• Design and build petabyte scale systems for high availability, high throughput, data consistency, security, and end user privacy, defining our next generation of data analytics tooling
• You will mentor other engineers and promote software engineering best practices across the organization designing systems with monitoring, auditing, reliability, and security at their core.
• Come up with solutions for scaling data systems for various business needs and collaborate in a dynamic and consultative environment.

Education & Qualifications

Minimum Qualifications

• Minimum 8+ years of software development or DevOps experience with minimum bachelor’s degree in Computer Science or Engineering.
• 8+ years of Cloud IaC with deep expertise in Terraform/Cloudformation & Ansible/Salt deployment and 2+ years of Kubernetes experience focused on DevOps
• Practical experience with Data Engineering and the accompanying DevOps & DataOps workflows.
• A deep understanding of CI/CD tools and a strong desire to help teams release frequently to production with a focus on creating reliable high-quality results.
• Extensive experience building large scale distributed systems and data analytics processes on cloud

native, in-memory, and fit-for-purpose hybrid infrastructure. Experience with cybersecurity data and

globally distributed log & event processing systems with data mesh and data federation as the

architectural core is highly desirable.

• Expertise in DevOps, DevSecOps and emergent experience with DataSecOps and Data Governance

practices — deep experience with managing and scaling container-based infrastructure-as-code

technologies from the CNCF and related orbits.

• Experience in big data technologies like Presto/Trino, Spark & Flink, Airflow & Prefect, RedPanda & Kafka, Iceberg & Delta Lake, Cassandra & Scylla, PlanetScale Vitess & CockroachDB, Snowflake & Databricks,

MemGraph & Neo4J as well as modern security tooling like Splunk, Panther, Datadog, Elastic, Arcsight etc.

• Experience designing and building data warehouse, data lake or lake house using batch, streaming,

lambda and data mesh solutions and with improving efficiency, scalability, and stability of system

• Knowledge of Dbt, Airflow, Ansible, Argo, or other data pipeline systems; ideally experience building and maintaining a data warehouse and understanding of simple data science workflows and terminology.
• Expertise with either AWS, GCP, Azure, and Services/Tooling such as or similar to: Terraform, Packer, Docker, Kubernetes, Helm, Prometheus, Grafana, Fluent Bit, Istio (Service Mesh)
• Strong background integrating continuous delivery(CD) with Kubernetes using tools such as Argo, GitLab, Spinnaker and strong Git experience, development methodologies, trunk-based develop vs. git flow, etc.
• Strong end-to-end ownership and a good sense of urgency to enable proper self-prioritization

Preferred Experience

• 10+ years of experience with Python, Java, or similar languages, with cloud infrastructure (e.g. AWS, GCP, Azure), and deep experience working with big data processing infrastructures and cloud architecture
• Deep experience with cloud devops tooling and expertise in container native systems and associated

security and scaling considerations – ability to work with and build tooling that works in a multi/hybrid

cloud environment with modern CI/CD, IaC, DataOps, and DevSecOps best practices.

• Have built and scaled hybrid cloud and streaming data and ML platforms from scratch and are up-to-date with tools and methods for deploying, managing and connecting data mesh in complex enterprise environments
• Experience working or developing Kubernetes autoscaling tools and deep experience with container and cloud security principles
• Deep experience with designing and delivering data platforms with in-built lineage, federation, governance, compliance, security, and privacy.
• Experience with building and delivering MLOps and Data Ops platforms and familiarity with processes for MRM coordination, deployment,  and monitoring of production grade ML models  in a regulated high-growth tech environment