Data Engineer

JPS Tech Solutions; Seattle (WA); On-site; USD 170,000 - 210,000; Full time

Job summary

JPS Tech Solutions is seeking a Senior Data Engineer to join an onsite data engineering team in Seattle. You will drive end-to-end data solutions, leveraging Databricks, PySpark, and AWS-based ETL pipelines to modernize a large data platform.

You will independently design, build, and optimize pipelines, engage with architects and stakeholders, and contribute to an Agile environment. This is a hands-on role requiring deep technical expertise and strong communication skills.

Qualifications

• 12+ years of IT experience with a data engineering focus.
• Hands-on expertise with Databricks, PySpark, SQL, and AWS-based ETL pipelines.
• Ability to independently design and build ETL pipelines and optimize SQL queries.
• Experience deploying production code using Git and CI/CD pipelines.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines using Databricks and PySpark.
• Lead migration of data pipelines from on-premises environments to AWS.
• Build robust data ingestion frameworks for structured, semi-structured, and unstructured data sources.
• Write, optimize, and maintain complex SQL queries across RDBMS, data lakes, and federated data environments.
• Review existing data solutions and recommend architectural improvements for performance, scalability, and maintainability.
• Collaborate with data architects, analysts, and business stakeholders to translate requirements into technical solutions.

Skills

Databricks

PySpark

SQL

Python

AWS-based ETL pipelines

Git & CI/CD

Education

Bachelor's degree in Computer Science

Tools

AWS services (EC2, S3, EMR)

Databricks

Apache Spark / PySpark

Job description

Job Description

We are seeking a Senior Data Engineer to join a highly collaborative, onsite data engineering team supporting large-scale cloud migration and data platform modernization initiatives. This is a hands-on, end-to-end role requiring deep expertise in Databricks, PySpark, SQL, and AWS-based ETL pipelines.

The ideal candidate is a senior practitioner who can not only build and deliver solutions independently but also evaluate existing architectures, identify gaps, and propose modern, scalable approaches aligned with Databricks and cloud data engineering best practices. You will work in an Agile environment, partnering closely with engineering peers, architects, and business stakeholders.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines using Databricks and Apache Spark (PySpark)
• Lead and support migration of data pipelines and applications from on-premises environments to AWS
• Build robust data ingestion frameworks for structured, semi-structured, and unstructured data sources
• Write, optimize, and maintain complex SQL queries across RDBMS, data lakes, and federated data environments
• Review existing data solutions and recommend architectural improvements for performance, scalability, and maintainability
• Develop reusable frameworks and components to standardize data processing patterns
• Tune and optimize Spark jobs for performance and cost efficiency in cloud environments
• Build, deploy, and support solutions end-to-end, from development through production
• Implement CI/CD pipelines and follow version control best practices
• Enforce data quality, validation, security, and governance standards
• Collaborate with data architects, analysts, and business stakeholders to translate requirements into technical solutions
• Participate in Agile ceremonies, including sprint planning, estimation, and retrospectives
• Troubleshoot, debug, and resolve production pipeline issues
• Take full ownership of solutions from design through production support

Required Qualifications

• 12+ years of overall IT experience with strong focus on data engineering
• Hands-on expertise with Databricks SaaS, Python, and PySpark
• Ability to independently design and build ETL pipelines
• Expert-level SQL skills, including writing and optimizing complex queries
• Strong experience with AWS-based ETL services, including:
• AWS Glue

1. EC2
2. EMR
3. Amazon S3

• Solid understanding of Data Lake and Lakehouse architectures
• Experience working with RDBMS and large-scale analytical datasets
• Proven experience deploying production code using Git and CI/CD pipelines
• Comfortable working onsite full-time in a collaborative team environment
• Strong communication skills with a solution-oriented mindset
• Ability to own solutions end to end, from architecture through operations

Education

• Bachelor’s degree in Computer Science or equivalent professional experience