Remote Data Engineer (L4) – Privacy‑Focused Data Pipeline Development & Security Architecture – careerzynith

```html About careerzynith careerzynith is a global leader in streaming entertainment, serving more than 230 million members across 190+ countries. With a rapidly expanding portfolio that now includes a subscription‑supported ad tier, immersive gaming experiences, and live sports, careerzynith is at the forefront of digital media innovation. This growth fuels an unprecedented increase in data volume, variety, and velocity, creating a unique opportunity for engineers who are passionate about building robust, privacy‑first data infrastructures. Our mission is simple yet ambitious: deliver world‑class entertainment while safeguarding the privacy and security of every member’s data. To achieve this, careerzynith invests heavily in cutting‑edge data platforms, rigorous compliance frameworks, and a culture that encourages bold experimentation and continuous learning. Role Overview We are seeking a highly motivated Data Engineer (L4) – Privacy to join our Security and Privacy Engineering team. In this remote, full‑time position based in California, you will design, implement, and maintain scalable data pipelines that power privacy‑centric analytics and compliance reporting. You will work closely with product, security, and legal stakeholders to ensure that data is handled responsibly, securely, and in line with global regulations.

Key Responsibilities

• Engineer efficient, flexible, and resilient data pipelines to ingest, transform, and store both structured and unstructured data from sources such as Kafka, Flink, Hive, and Flash.
• Develop and maintain ETL jobs that aggregate and anonymize user‑level data to support privacy‑impact assessments and regulatory reporting.
• Collaborate with the security architecture team to translate product privacy requirements into concrete data models and processing workflows.
• Design and enforce data‑access controls, ensuring that only authorized services and personnel can view sensitive information.
• Continuously monitor, profile, and optimize existing pipelines for performance, cost‑efficiency, and compliance adherence.
• Participate in code reviews, design discussions, and incident response drills to uphold the highest standards of data security.
• Document data lineage, schema evolution, and privacy safeguards to provide clear audit trails for internal and external auditors.
• Mentor junior engineers and share best practices around privacy‑by‑design, data governance, and modern data engineering techniques.

Essential Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
• Minimum 2 years of professional experience in data engineering or software development.
• Proficiency in at least one programming language such as Java, Python, or Scala, with a strong emphasis on clean, maintainable code.
• Solid experience writing advanced SQL queries and working with relational databases.
• Hands‑on experience building large‑scale data pipelines using technologies like Hive, Flink, Kafka, and Flash.
• Demonstrated ability to translate business and privacy requirements into scalable data models and processing logic.
• Strong analytical mindset with a keen eye for data quality, security implications, and performance bottlenecks.
• Excellent communication skills and the ability to work effectively in a fast‑paced, remote environment.

Preferred Qualifications

• Experience with privacy‑enhancing technologies such as differential privacy, tokenization, or data masking.
• Familiarity with cloud data platforms (e.g., AWS, GCP, Azure) and infrastructure‑as‑code tools.
• Knowledge of GDPR, CCPA, or other international data‑protection regulations.
• Previous work on security‑focused data architectures or compliance pipelines.
• Contribution to open‑source projects or technical blogs related to data engineering or privacy.

Core Skills & Competencies

• Data Modeling: Ability to design normalized and denormalized schemas that balance performance with privacy constraints.
• Pipeline Orchestration: Experience with workflow managers such as Airflow, Luigi, or Prefect.
• Problem Solving: Proactive approach to identifying risks, troubleshooting failures, and implementing robust fixes.
• Collaboration: Comfortable partnering with cross‑functional teams—including security, legal, product, and analytics—to achieve shared goals.
• Continuous Learning: Eagerness to stay current with emerging data technologies, pri