Data Engineer

Unisys; Seattle (WA); On-site; USD 100,000 - 130,000; Full time

Job summary

Unisys is seeking an AWS Data Engineer to design, build, and optimize data pipelines on AWS. This remote role involves managing the end-to-end data lifecycle, ensuring high-quality and accessible data for analytics. Candidates should have a Bachelor's degree in Data Engineering or a related field, along with over 5 years of experience. Proficiency in SQL and Python for data processing is required. The position offers an opportunity to work with data extracted from various sources and collaborate with Data Scientists and analysts.

Qualifications

• 5+ years of experience in data engineering roles.
• Proficiency in SQL, Python, or Scala for data transformation and processing.
• Experience in the utility industry data including meter data, customer data, grid/asset data.

Responsibilities

• Design, build, and optimize ETL/ELT workflows to ingest data from multiple sources.
• Automate batch and streaming data pipelines for real-time analytics.
• Ensure pipelines are optimized for scalability, performance, and fault tolerance.

Skills

SQL

Python

Data Transformation

Data Engineering

AWS

Real-time Analytics

Education

Bachelor’s degree in Computer Science, Data Engineering, or related field

Tools

Databricks

Terraform

AWS Services (S3, Redshift, EMR)

Job description

Unisys is an Information Technology and Business Consulting firm providing project-based solutions, software solutions, and professional staffing services. –

Those authorized to work in the United States are encouraged to apply. We are unable to sponsor at this time.

Job Description

Client is seeking an AWS Data Engineer to design, build, and optimize large-scale data pipelines and analytics solutions on Amazon Web Services (AWS). The role will design and build the infrastructure and pipelines that enable organizations to collect, store, process, and analyze large volumes of structured and unstructured data efficiently and securely. A Data Engineer is responsible for the end‑to‑end data lifecycle, from ingestion and transformation to storage and delivery for analytics, machine learning, and operational systems. They ensure data is reliable, high‑quality, scalable, and accessible for business and technical stakeholders.

Location

This position is remote.

Responsibilities

• Design, build, and optimize ETL/ELT workflows to ingest data from multiple sources (e.g., S3, Redshift, Lake Formation, Glue, Lambda).
• Implement data cleansing, enrichment, and standardization processes.
• Automate batch and streaming data pipelines for real‑time analytics. Build solutions for both streaming (Kinesis, MSK, Lambda) and batch processing (Glue, EMR, Step Functions).
• Ensure pipelines are optimized for scalability, performance, and fault tolerance.
• Optimize SQL queries, data models, and pipeline performance.
• Ensure efficient use of cloud‑native resources (compute, storage, networking).
• Design and implement data architecture across data lakes, data warehouses, and lakehouses.
• Implement data integration from diverse sources (databases, APIs, IoT, third‑party systems).
• Work with Data Scientists, Analysts, and BI developers to deliver clean, well‑structured data.
• Document data assets and processes for discoverability.
• Training of existing core staff who will maintain infrastructure and pipelines.

Required Education & Experience

• Bachelor’s degree in Computer Science, Data Engineering, or related field.
• 5+ years of experience in data engineering roles.
• Proficiency in SQL, Python, or Scala for data transformation and processing.
• Experience in the utility industry data including meter data, customer data, grid/asset data, work management, outage data.
• Familiarity with IEC CIM standards and utility integration frameworks.
• Working knowledge of Databricks on AWS.
• Working knowledge of DevOps and CI/CD.

Preferred Qualifications

• Experience with IaC tools (e.g. Terraform) is a plus.