Sr. Data Engineer- Databricks/PySpark

Innovya Technologies

Pasadena (CA)

On-site

USD 140,000 - 180,000

Full time

2 days ago
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Job summary

Innovya Technologies seeks a Sr. Data Engineer – PySpark/Databricks/Python in Pasadena, CA, to design, build, and maintain scalable data pipelines and robust ETL workflows. You will optimize data models and support warehousing for analytics, collaborating with analysts, scientists, and product teams to ensure high-quality data access and governance.

The role is full-time and on-site, requiring strong Cloud data platform experience and a proactive approach to performance and security concerns.

Qualifications

  • Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field; equivalent experience considered.
  • Proven experience building scalable data pipelines and data architectures.

Responsibilities

  • Design, build, and maintain scalable data pipelines using PySpark, Databricks, and Python.
  • Develop and optimize data models, and support data warehousing solutions for analytics and reporting.
  • Collaborate with data analysts, data scientists, and product teams to improve data accessibility and governance.

Skills

Data engineering
PySpark
Databricks
Python
ETL pipelines
Data modeling
Data warehousing
Cloud data platforms
SQL
NoSQL
Data governance

Education

Bachelor's degree in Computer Science / Information Systems / Engineering

Tools

Databricks

Job description

Onsite Interview is Mandatory - No exceptions.

Company Description Innovya Technologies is a technology services company focused on accelerating business value from digital initiatives. The organization brings together experts who rapidly understand client needs and drive end-to-end product and feature development. Innovya operates across multiple segments, including RPA deployments, game development, web and mobile applications, and cloud implementations. Team members collaborate with diverse industries and projects, offering opportunities to work with modern tools and emerging technologies. The company values innovation, practical problem-solving, and measurable outcomes for its clients.

Role Description The Sr. Data Engineer – PySpark/Databricks/Python is a full-time, on-site role based in Pasadena, CA. This role is responsible for designing, building, and maintaining scalable data pipelines using PySpark, Databricks, and related cloud data platforms. Day-to-day work includes implementing robust ETL workflows, developing and optimizing data models, and supporting data warehousing solutions to ensure reliable, high-quality data for analytics and reporting. The individual will collaborate closely with data analysts, data scientists, and product teams to understand data requirements, improve data accessibility, and enhance overall data architecture. The role also involves performance tuning, monitoring data systems, ensuring data security and governance, and contributing to best practices and documentation.

Qualifications
  • Strong Data Engineering skills, including experience building and maintaining scalable data pipelines.
  • Proficiency in Data Modeling and Data Warehousing to design efficient, reliable data structures and storage solutions.
  • Hands-on experience with Extract Transform Load (ETL) processes and frameworks to integrate data from multiple sources.
  • Applied Data Analytics skills to support reporting, dashboards, and data-driven decision-making.
  • Advanced experience with PySpark, Databricks, and Python for data processing and automation.
  • Experience with cloud data platforms (e.g., AWS, Azure, or GCP) and distributed processing frameworks.
  • Knowledge of SQL and NoSQL databases, performance tuning, and data optimization techniques.
  • Understanding of data governance, data quality, and security best practices.
  • Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field; equivalent experience considered.
  • Ability to work collaboratively with cross-functional teams and communicate complex data concepts clearly.
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