AIML - Sr Data Engineer, DMLI

Socket.dev; Cupertino (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Socket.dev is seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines and data platforms. You will enable reliable analytics, ML, and business intelligence across the organization, leveraging Python and distributed systems in a modern data stack.

You will own end-to-end data engineering, working with BigQuery, Dataflow, Pub/Sub, and Airflow, while focusing on performance, cost optimization, and robust data modeling.

Qualifications

• Bachelor’s or Master’s degree in CS/Engineering or equivalent practical experience.
• 5+ years in data engineering or distributed systems with large-scale batch and streaming pipelines.
• Proficient in Python, CI/CD, testing, and code reviews.
• Deep experience with GCP data ecosystem: BigQuery, Dataflow, Pub/Sub, Composer (Airflow).

Responsibilities

• Design and build scalable data pipelines and data platforms.
• Maintain data integration solutions enabling analytics, ML, and BI across the organization.
• Optimize BigQuery performance and cost; model data at scale.

Skills

Python

CI/CD

Testing

Code reviews

Distributed systems

Data modeling

Education

BSc/BA or MSc/MA in CS/Engineering

Tools

BigQuery

Dataflow

Pub/Sub

Airflow

Spark

Dataproc

Git

Job description

DESCRIPTION

We are looking for a skilled Senior Data Engineer to join our engineering team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data platforms, and data integration solutions that enable reliable analytics, machine learning, and business intelligence capabilities across the organization.

MINIMUM QUALIFICATIONS

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience). Strong software engineering foundation with 5+ years of experience in data engineering, backend engineering, or distributed systems. Strong hands-on experience designing and building large-scale batch and streaming data pipelines using Python and distributed systems. Expertise in ETL/ELT development, event-driven architectures, and working with high-volume datasets using technologies such as Pub/Sub, Dataflow (Apache Beam), and Spark-based processing (Dataproc or equivalent). Deep expertise in the Google Cloud Platform data ecosystem, including BigQuery, Dataflow, Pub/Sub, and Composer (Airflow). Proven ability to design, optimize, and operate scalable data platforms, with strong experience in BigQuery performance tuning, cost optimization, data modeling, partitioning, clustering, and query optimization at scale. Proficient in Python, version control, CI/CD practices, testing, and code reviews.

PREFERRED QUALIFICATIONS

7+ years of experience in data engineering or large-scale distributed systems. Experience designing end-to-end data platforms or data mesh architectures. Strong understanding of data governance, data lineage, and metadata management frameworks. Experience supporting ML pipelines and MLOps workflows, including feature engineering and training data generation. Experience building systems with strong reliability, observability, and SLAs (monitoring, alerting, debugging distributed pipelines). Familiarity with Generative AI / LLM-based systems, including: LLM-powered data workflows, Agentic pipelines, Embedding/vector-based retrieval systems. Experience influencing architecture decisions across teams and driving technical direction. Experience collaborating in cross-functional, fast-paced tech environments or cloud-native organizations with a focus on building reliable, maintainable, and production-grade data systems.