Data Science Engineering Lead
3 semanas atrás
Data Science Lead Opportunity
We are seeking an experienced Data Science Lead to join our Cyber Security Data Science, Analytics, and Intelligent Automation practice at Johnson & Johnson.
This role will involve engineering and development of automated data pipelines, sophisticated data models, and on-demand analytics for various Cybersecurity use cases, including Threat Detection and Monitoring, Cyber Risk Insights and Management, and Vulnerability Management Process.
Responsibilities
- Engage with customers to frame and understand their data use cases and value propositions.
- Develop scalable and automated data pipelines and data models for Cybersecurity threat detection and product metrics use cases.
- Identify, design, implement, and support sophisticated data engineering processes for greater scalability, optimizing data delivery, and automating manual processes.
- Document dataflows, data lineages, code, and logic and share with the team.
- Develop CI/CD Infrastructure as Code streams to bring data pipelines from sandbox to DEV to PROD environments.
- Identify data governance needs, being responsible for the design of data modeling and handling procedures to ensure compliance with applicable laws and policies.
- Work with partners, including the Executive, Product, SOC, Data, and Design teams, to support data and reporting needs while assisting with data-related technical issues.
- Deliver quality work and outstanding customer service in a high-pressure, fast-paced setting to meet company needs.
- Responsible for key vendor and customer relationships across platforms and services, setting up and supervising clear product SLA, measurable performance, product health monitoring, and continuous improvement.
Qualifications
- Expert in Python and PySpark knowledge with at least 5 years of experience developing end-to-end ETL pipelines.
- Expert SQL knowledge and experience with SQL scripting in various DB technologies (MS SQL, Hive/Impala, Oracle, Presto/Athena SQL, Azure KQL).
- Solid understanding in either Azure or AWS data analytics and engineering technology stack (S3, Glue, Lambda, Step Functions, IAM, Redshift, VPC, Azure Data Explorer, Azure Data Factory, Azure Cosmos DB, Azure ADX, Azure Synapse Analytics, or Data Bricks).
- Working experience with different file formats: JSON, parquet, CSV, text.
- Strong GIT/Code version controlling experience, knowledge of overall related tools, commands, and workflows.
- Strong data modelling/architecture, able to develop data mart, star schema, and related ETL.
- Knowledge of IT technical infrastructures (Network, IAM, Endpoints).
- Demonstrated experience in all phases of managing data projects, including problem definition, solution formulation, model building, productizing, and delivering measurable impact.
- Good communicator with excellent verbal and written presentation skills, able to voice support for data-informed decision-making at all levels.
- Ability to work independently, in a fast-paced environment, and prioritize in parallel while leading expectations.
- Must have a strong customer service orientation, eye for business value, and a bias for action.
- Preferred qualifications include experience with developing data models for Cybersecurity threat and risk detection and monitoring, an advanced degree, knowledge of Terraform, DevOps CI/CD, and Jira, and Hadoop working experience, especially Hive, HDFS, Impala.
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