Sr Data Engineer, Quincy, MA – hybrid (NO H1B, CPT, OPT)

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Please find the attached Job Description. If you feel comfortable then please send me your updated resume or call me back on 512-898-7112.

Position: Sr Data Engineer

Location: Quincy, MA – hybrid

Duration: 6+months Contract

Interview Process: Video

Job Description:

Data Integration & API Development: Integrate cybersecurity data sources and build data APIs for real-time insights and streamlined access.
Pipeline Engineering: Design and optimize large-scale ETL/ELT pipelines on Databricks using Python and PySpark.
Data Quality & Governance: Implement automated data checks, manage data lineage, and ensure compliance with cybersecurity standards.
Analytics & Visualization: Create data models, visualizations, and dashboards for threat and vulnerability insights using Databricks and React.js.
Data Architecture: Develop secure, scalable environments on AWS (S3, ELB, Lambda) for data storage and processing.
CI/CD for Data & Security: Use CI/CD pipelines to automate testing and deployment in alignment with Agile practices.
ML Integration: Deploy ML models for threat detection, anomaly detection, and risk scoring.
Mentorship: Guide junior engineers, establish best practices, and foster a high-performance, innovative culture.

Qualifications:

The CASM Platform Product & Engineering team is looking for a Sr. Data Engineer, Cyber AI App & Attack Surface Management. CASM Platform team is focused on building enterprise-level robust and scalable Cyber AI applications and Platform to manage vulnerability, risk, security configurations, and attack surfaces oversight across the bank. We are seeking an experienced, hands-on Senior Data Engineer to join our CASM Platform team. Ideal
candidates bring a deep understanding of data engineering within a cybersecurity context, extensive
experience with cloud-native tools (especially AWS), and a passion for automation, security, intelligent
orchestration, and resilience. You’ll work in high-paced Agile Product Engineering cross-functional
teams, including Product, AI application, DevOps, and Data Quality Management (DQM), to transform
the CASM platform into a single pane of glass for secure and insightful network asset and risk visibility.
We have multiple openings for this role, and it is open to candidates with varying levels of experience.

Role Responsibilities
As a Senior Data Engineer, CASM Platform, you will:

Data Integration, API Development: Integrate diverse cybersecurity data sources using variety of API mechanisms and to standardize and streamline data across the data and user planes.
Build and maintain data APIs for seamless access to data pipelines, enabling real-time insights for applications, machine learning models, and analytical layers.
Data Pipeline Engineering Optimization: Design, develop, and optimize large-scale ETL/ELT pipelines on Databricks to efficiently process and transform cybersecurity data. Utilize Python, PySpark, and Databricks to automate and standardize data workflows across stages (raw, cleaned, curated), ensuring scalability and high performance.
Data Quality & Governance: Implement automated data quality checks, leveraging Databricks DQM tools and CI/CD pipelines to uphold data integrity and governance standards. Ensure data lineage, metadata management, and compliance with cybersecurity and privacy regulations, applying rigorous quality standards across data ingestion and processing workflows.
Data Analytics & Visualization: Design centralized data models and perform in-depth data analysis to support cybersecurity and risk management objectives. Develop visualizations and dashboards using tools like Databricks, encapsulate data to spin up to React.js application layer to provide stakeholders with actionable insights into threat landscapes, vulnerability trends, and performance metrics across the platform.
Scalable & Secure Data Architecture: Architect and manage secure, high-performance data environments on Databricks, utilizing AWS services such as S3, ELB, and Lambda. Ensure data availability, consistency, and security, aligning with AWS best practices and data encryption standards to safeguard sensitive cybersecurity data.
Agile Product; Engineering Continuous Delivery: Collaborate with advanced Agile Product; Engineering cross-functional teams to deliver data-driven insights through analytics tools and custom visualizations that inform strategy and decision-making. Empower stakeholders with timely, actionable intelligence from complex data analyses, enhancing their ability to respond to evolving cybersecurity risks.
Data Science & ML Integration: deploy machine learning models, including predictive analytics, anomaly detection, and risk scoring algorithms, into the CASM platform. Leverage Python and PySpark to enable real-time and batch processing of model outputs, enhancing CASM Platform’s proactive threat detection and response capabilities.
Mentorship; Best Practices Promotion: Mentor junior engineers, establishing best practices in data engineering, DevOps, data science, and analytics. Encourage high standards in model deployment, data security, performance optimization, and visualization practices, fostering a culture of innovation and excellence.

Education Qualifications:
Minimum Qualifications
• Education: B.S., M.S., or Ph.D. in Computer Science, Data Science, Information Systems, or a
related field, or equivalent professional experience.
• Technical Expertise: 8+ years in data engineering with strong skills in Python, PySpark, SQL,
and extensive, hands-on experience with Databricks and big data frameworks. Expertise in
integrating data science workflows and deploying ML models for real-time and batch processing
within a cybersecurity context.
• Cloud Proficiency: Advanced proficiency in AWS, including EC2, S3, Lambda, ELB, and
container orchestration (Docker, Kubernetes). Experience in managing large-scale data
environments on AWS, optimizing for performance, security, and compliance.
• Security Integration: Proven experience implementing SCAS, SAST, DAST/WAS, and secure
DevOps practices within an SDLC framework to ensure data security and compliance in a high-
stakes cybersecurity environment.
• Data Architecture: Demonstrated ability to design and implement complex data architectures,
including data lakes, data warehouses, and lake house solutions. Emphasis on secure, scalable,
and highly available data structures that support ML-driven insights and real-time analytics.
• Data Quality ; Governance: Hands-on experience with automated data quality checks, data
lineage, and governance standards. Proficiency in Databricks DQM or similar tools to enforce
data integrity and compliance across pipelines.
• Data Analytics; Visualization: Proficiency with analytics and visualization tools such as
Databricks, Power BI, and Tableau to generate actionable insights for cybersecurity risks, threat
patterns, and vulnerability trends. Skilled in translating complex data into accessible visuals and
reports for cross-functional teams.
• CI/CD and Automation: Experience building CI/CD pipelines that automate testing, security
scans, and deployment processes. Proficiency in deploying ML models and data processing
workflows using CI/CD, ensuring consistent quality and streamlined delivery.
• Agile Experience: Deep experience in Agile/Scrum environments, with a thorough
understanding of Agile core values and principles, effectively delivering complex projects with
agility and cross-functional collaboration.

Preferred Experience:

• Advanced Data Modeling; Governance: Expertise in designing data models for cybersecurity
data analytics, emphasizing data lineage, federation, governance, and compliance. Experience
ensuring security and privacy within data architectures.
• Machine Learning; Predictive Analytics: Experience deploying ML algorithms, predictive
models, and anomaly detection frameworks to bolster CASM platform’s cybersecurity
capabilities.
• High-Performance Engineering Culture: Background in mentoring engineers in data
engineering best practices, promoting data science, ML, and analytics integration, and fostering
a culture of collaboration and continuous improvement.