Sr. Data Engineer

Techgene Solutions; Pasadena (CA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Techgene Solutions in Pasadena, CA is seeking an experienced Data Engineer to design and maintain scalable data pipelines using Databricks, PySpark, and Python. The role focuses on ETL/ELT, data lakehouse architectures, and cloud-based processing.

You will collaborate with Data Architects, Data Scientists, and BI developers, implement CI/CD for data code, and ensure data quality, governance, and security across pipelines.

Qualifications

• Proven experience building scalable data pipelines with Databricks and PySpark.
• Strong Python development skills for data processing workflows.
• Experience with Delta Lake and data lakehouse architectures.
• Hands-on knowledge of Spark performance tuning and optimization.
• Experience implementing CI/CD for data engineering code and git workflows.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks, PySpark, and Python.
• Develop ETL/ELT workflows to ingest, transform, cleanse, and integrate data from multiple sources.
• Build and optimize data processing jobs using PySpark and Spark SQL.
• Work with Delta Lake for storage, transformation, and incremental processing.
• Collaborate with data architects and stakeholders to understand data requirements.

Skills

Databricks

PySpark

Python

Delta Lake

Spark SQL

Git

CI/CD

Cloud Platforms (AWS/GCP/Azure)

Job description

Work Arrangement: Hybrid - 3 Days/Week Onsite

Job Summary

We are looking for an experienced Data Engineer with strong expertise in Databricks, PySpark, and Python to design, develop, and maintain scalable data engineering solutions. The ideal candidate will have hands-on experience building ETL/ELT pipelines, data processing frameworks, and data lake/lakehouse solutions using Databricks and cloud technologies.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks, PySpark, and Python.
• Develop ETL/ELT workflows to ingest, transform, cleanse, and integrate data from multiple sources.
• Build and optimize data processing jobs using PySpark and Spark SQL.
• Work extensively with Databricks Lakehouse, Delta Lake, notebooks, workflows, and clusters.
• Develop reusable Python modules and frameworks for data processing and automation.
• Implement data quality checks, validation, error handling, and monitoring within data pipelines.
• Optimize Spark jobs, including partitioning, caching, joins, and performance tuning.
• Work with Delta Lake for data storage, transformation, versioning, and incremental processing.
• Integrate data from relational databases, APIs, files, cloud storage, and other enterprise data sources.
• Collaborate with Data Architects, Data Scientists, BI Developers, and business stakeholders to understand data requirements.
• Implement CI/CD and source-control practices for data engineering code.
• Troubleshoot production data pipeline failures and perform root cause analysis (RCA).
• Ensure data security, governance, lineage, and compliance requirements are followed.
• Participate in design discussions, code reviews, testing, deployment, and production support.

Required Skills

• Strong hands-on experience with Databricks
• Strong PySpark / Apache Spark experience
• Experience with Delta Lake
• Experience with data lake/lakehouse architecture
• Experience with Spark performance tuning and optimization
• Experience working with large-volume datasets
• Strong understanding of data modeling and data engineering concepts
• Experience with Git and CI/CD
• Experience with cloud platforms such as AWS, Azure, or GCP