WeAreDevelopers Logo WeAreDevelopers
Sign in
For You LIVE
Back

Senior Data Engineer

Rays Techsolutions Inc.
San Jose, CA, United States
13 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours
Job source
www.dice.com

Tech stack

Airflow Amazon Web Services Amazon S3 Data Analysis Cloud Computing Security Continuous Integration Data Architecture Information Engineering Data Infrastructure Extract Transform Load (ETL) Data Systems Python (Programming Language)
+6 more
SQL Databases Git Data Lakes Pyspark Data Pipelines Databricks

Job description

We are seeking a Senior Data Engineer with strong expertise in Databricks, AWS, PySpark, and Apache Airflow to build scalable data pipelines and modern data platforms that support analytics and business-critical applications., * Design and maintain scalable ETL/ELT pipelines using Databricks and PySpark.

  • Build and manage data workflows using Apache Airflow.
  • Develop and optimize data solutions on AWS (S3, Glue, Lambda, EMR, Redshift).
  • Ensure data quality, performance, security, and governance.
  • Collaborate with cross-functional teams to deliver reliable and scalable data solutions.
  • Mentor junior engineers and contribute to engineering best practices.

Requirements

  • 8 10 years of Data Engineering experience.
  • Strong hands-on experience with Databricks, AWS, PySpark, Python, SQL, and Apache Airflow.
  • Experience with Delta Lake, Lakehouse Architecture, ETL/ELT pipelines, and data modeling.
  • Knowledge of GenAI, analytics, Git, CI/CD, and cloud security best practices.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.dice.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

2:15 min

Empowering domain teams with an open data platform

Sandhya Menon Sandhya Menon · World Congress 2026 Europe

6:21 min

Investigating push inefficiencies with upstream Git experts

Jonathan Creamer · Coffee With Developers

4:32 min

Harnessing Spark with Python using PySpark and Py4J

Ayon Roy · LIVE

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

2:57 min

Core technical practices for robust data engineering

Sandhya Menon Sandhya Menon · World Congress 2026 Europe

3:37 min

Scaling machine learning pipelines from prototypes to petabytes

Julian Joseph · LIVE

Videos

See all
From Messy Queries to Scalable Systems - How Data Engineering actually works thumbnail
31:15
World Congress 2026 Europe • about 1 month ago

From Messy Queries to Scalable Systems - How Data Engineering actually works

Sandhya Menon

PySpark - Combining Machine Learning & Big Data thumbnail
43:26
LIVE • almost 6 years ago

PySpark - Combining Machine Learning & Big Data

Ayon Roy

Data Science in Retail thumbnail
58:21
LIVE • about 3 years ago

Data Science in Retail

Julian Joseph

How a Small Team Shrank a Microsoft Monorepo by 94% thumbnail
33:23
Coffee With Developers • almost 2 years ago

How a Small Team Shrank a Microsoft Monorepo by 94%

Jonathan Creamer

Enjoying SQL data pipelines with dbt thumbnail
26:53
World Congress 2023 • almost 3 years ago

Enjoying SQL data pipelines with dbt

Matthias Niehoff

Empowering Retail Through Applied Machine Learning thumbnail
22:41
World Congress 2024 • almost 2 years ago

Empowering Retail Through Applied Machine Learning

Christoph Fassbach, Daniel Rohr

Related articles

See all