Security Data Engineer - USDS

- Develop expertise in the USDS data to build and own relevant pipelines/ETLs.
- Own data integrity, availability, transformation logic, and efficient data access to support the growing needs of the organization.
- Identify gaps in existing data, create data product specs, and implement data tracking.
- Incorporate automation wherever possible to improve data pipelines and analyses.
- Build and maintain documentation to ensure data accessibility
- Be our go-to data expert and have a deep understanding of our data warehouse and data processing layers.
- Be a primary point of contact for ad hoc data requests and reporting

Qualifications

Minimum Qualifications:
- Bachelors degree in Statistics, Economics, Computer Science or another quantitative field
- 5+ years of experience working with data analytics and data engineering, including experience with data cleaning and preprocessing, data analysis and dashboard development.
- Proficiency in distributed data processing using Big Data technologies like Spark/Scala, Hadoop/HDFS/AWS/S3, Cassandra and Kafka.
- Proficiency in ETL , data warehousing, data modeling, data design, SQL, and NoSQL databases
- Proactive, self-driven and impact driven person
- Comfortable with ambiguity; able to thrive with minimal oversight and process

Preferred Qualifications
- Proficiency in Python for Data Science: Strong command of Python libraries essential for data manipulation, analysis, and feature engineering, including Pandas and NumPy.
- Familiarity with Machine Learning Concepts & Libraries: Understanding of core machine learning concepts (e.g., supervised, unsupervised learning, model evaluation) and practical experience with libraries like Scikit-learn for building and integrating basic ML workflows into data pipelines.
- Exposure to Deep Learning Frameworks (e.g., PyTorch, TensorFlow): While not necessarily building models from scratch, an understanding of how these frameworks consume and process data is highly beneficial for designing data pipelines that feed into deep learning models (e.g., for anomaly detection, threat intelligence).
- Experience with MLOps Principles & Tools: Familiarity with concepts like feature stores, model versioning, and continuous integration/delivery for ML pipelines. Experience with tools like MLflow, Kubeflow, or similar platforms for managing the ML lifecycle is a strong plus.
- Data Preparation for AI/ML: Proven ability to prepare, clean, and transform large-scale datasets specifically for machine learning model training and inference, ensuring data quality and consistency.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $118658 - $259200 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

Client-provided location(s): New York, NY

Job ID: TikTok-7527311955372312850

Employment Type: OTHER

Posted: 2025-07-31T00:38:34