Senior Data Scientist

Jobot; Los Angeles (CA); On-site; USD 80,000 - 150,000; Full time

Benefits offered by this job

Paid Vacation Time

Health, Dental, and Vision Insurance

401(k) with employer match

Life Insurance

Job summary

A leading tech company in California is urgently hiring a Senior Data Scientist to build and operate the data infrastructure essential for analytics and machine learning. This role emphasizes designing lakehouse architecture and developing robust data pipelines. Ideal candidates should possess expertise in Python, SQL, and Apache Spark, with a significant focus on cloud environments like AWS or Azure. The position offers a competitive salary ranging from $80,000 to $150,000 per year along with comprehensive benefits.

Qualifications

• 8+ years of experience in data science or data engineering.
• Expert-level knowledge in Python and SQL with working knowledge of Scala/Java.
• 3–5+ years experience with Apache Spark and Databricks has been essential.

Responsibilities

• Architect and build data infrastructure supporting analytics and machine learning.
• Design and maintain lakehouse architecture on AWS/Azure.
• Develop data models and semantic layers for BI and production use.

Skills

Python

SQL

Apache Spark

Databricks

AWS

Azure

Data Modeling

CI/CD

Docker

Git

Education

8+ years in data science or data engineering

Tools

Airflow

Terraform

Power BI

Tableau

Looker

Job description

Senior Data Scientist

We are urgently hiring a Senior Data Scientist to architect, build, and operate the data infrastructure that powers analytics, machine learning, and operational reporting across the organization. This is a hands‑on role for a data scientist who thrives at the intersection of engineering, modeling, and production‑grade systems.

Salary: $80,000 – $150,000 per year.

What you’ll do

• Design and evolve our lakehouse architecture (Delta Lake on AWS/Azure), including storage layout, compute strategy (Databricks, EMR, Synapse), and performance SLAs.
• Build robust batch and streaming data pipelines using Apache Spark (PySpark/Scala), Databricks Workflows, Azure Data Factory, and event‑driven integrations (Kinesis, Event Hubs, Kafka).
• Develop and maintain analytical and ML‑ready data models, semantic layers, and dimensional schemas for BI and production use.
• Implement observability and reliability features including custom Spark metrics, anomaly detection, logging, and automated data quality checks.
• Optimize compute and storage performance through cluster tuning, caching, partitioning, and format selection, with measurable cost savings.
• Enforce data governance policies including RBAC, row‑level security, cataloging, lineage tracking, and compliance with GDPR, HIPAA/FHIR, and CCPA/CDPA.
• Collaborate with analytics, product, and platform teams to define SLAs, manage incident response, and guide data contract best practices.
• Mentor junior engineers and lead design reviews, setting standards for scalable, maintainable data systems.

What you bring

• 8+ years of experience in data science or data engineering.
• Expert‑level Python and SQL; working knowledge of Scala/Java for Spark.
• 3–5+ years of experience with Apache Spark and Databricks (including Delta Live Tables and performance tuning).
• Strong cloud experience in AWS (S3, EMR, Glue, Lambda, Kinesis) and/or Azure (ADLS Gen2, ADF, Event Hubs, Synapse).
• Experience with orchestration tools (Airflow, ADF) and transformation frameworks (dbt).
• Familiarity with data warehouses such as Snowflake, Redshift, or Databricks SQL.
• Proven track record of cost and performance optimization in cloud data environments.
• Solid foundation in data modeling, CI/CD, Docker/Linux, and Git.
• Ability to translate business needs into scalable data products and lead projects end‑to‑end.

Nice to have

• Experience with custom Spark listeners, low‑latency streaming, or event‑driven architectures.
• Exposure to healthcare data pipelines (FHIR) and DLT troubleshooting.
• Dashboarding experience with Power BI, Tableau, or Looker.
• Familiarity with governance frameworks and MLOps (feature stores, model monitoring).

Our stack

Databricks, Spark (PySpark/Scala), Delta Lake, Airflow, ADF, dbt, AWS (S3/EMR/Glue/Kinesis), Azure (ADLS/Synapse/Event Hubs), Terraform, GitHub Actions, Docker, CloudWatch, REST.

Success in 6–12 months looks like

• Reliable, observable pipelines with <1% failure rate and clear SLAs.
• 30–50% faster time‑to‑insight for key analytics use cases.
• Significant cost savings from optimized compute and storage.
• High stakeholder trust in platform reliability and alerting systems.

Benefits & Compensation

• Paid Vacation Time, Paid Sick Leave, Paid Holidays, Parental Leave.
• Health, Dental, and Vision Insurance.
• Employee Assistance Program.
• 401(k) with generous employer match.
• Life Insurance.

Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

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