Data Engineer

SFE; Seattle (WA); On-site; USD 120,000 - 140,000; Full time

Job summary

SFE is seeking a Data Engineer in Seattle, WA, to design, build, and maintain scalable data pipelines. The role focuses on ingesting and transforming data from multiple sources, using SQL and Python, with strong emphasis on ETL/ELT processes and cloud platforms.

The candidate will optimize data workflows, ensure data quality, and collaborate with data scientists and business stakeholders to deliver analytics-ready data for reporting and BI purposes. Onsite requirement applies.

Qualifications

• 6+ years of data engineering experience.
• 3+ years in data engineering, data science, or software engineering capacity.
• Expertise in robust data pipelines in SQL and Python.
• Experience with cloud-native solutions.
• 5+ years in data warehousing, modelling, and ETL/ELT at scale.
• Experience with AWS, GCP, or Azure.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for multi-source data ingestion and transformation.
• Develop data integration workflows using Python, SQL, APIs, batch and streaming technologies.
• Build and optimize data pipelines for structured, semi-structured, and unstructured data.
• Create data models, tables, views, procedures, and transformation logic for analytics and operations.
• Ingest data from databases, APIs, files, cloud storage, and enterprise apps.
• Work with cloud data platforms and services including data lakes and warehouses.
• Implement data quality checks, monitoring, logging, and error handling across pipelines.

Skills

Data pipelines

SQL

Python

Cloud platforms

Data warehousing

ETL/ELT

Airflow

Apache Spark

Kafka

NoSQL

Snowflake

Redshift

GCP

AWS

Azure

Data modelling

Job description

Section

Details

Job Title

Data Engineer

Job Type

Full Time

Client Location

Seattle, WA

Work Arrangement

Onsite

Duration

Full Time

Pay Rate / Salary

$120-$140k/yr.

Job Summary

• Experienced Data Engineer with strong expertise in designing, developing, and maintaining scalable data pipelines and data processing solutions.
• Skilled in data ingestion, transformation, integration, and migration across structured and unstructured data sources.
• Proficient in SQL, Python, ETL/ELT processes, cloud data platforms, data warehouses, and distributed data processing technologies.
• Experienced in building reliable data pipelines, optimizing data workflows, implementing data quality and governance practices, and collaborating with data scientists, analysts, application teams, and business stakeholders to deliver high-quality, analytics-ready data.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for ingesting and transforming data from multiple sources.
• Develop data integration workflows using Python, SQL, APIs, batch processing, and streaming technologies.
• Build and optimize data pipelines for structured, semi-structured, and unstructured datasets.
• Develop data models, tables, views, stored procedures, and transformation logic for analytical and operational requirements.
• Implement data ingestion from databases, APIs, files, cloud storage, and enterprise applications.
• Work with cloud data platforms such as AWS, Azure, or GCP and services including data lakes, data warehouses, and distributed processing frameworks.
• Perform data cleansing, validation, deduplication, enrichment, and transformation to improve data accuracy and consistency.
• Optimize SQL queries, data pipelines, and processing jobs for performance, scalability, and cost efficiency.
• Build and maintain data lake and data warehouse solutions supporting reporting, analytics, and business intelligence.
• Implement data quality checks, monitoring, logging, alerting, and error-handling mechanisms across data pipelines.

Required Qualifications

• 6+ years of data engineering experience
• 3+ year of experience working in a data engineering, data science, or software engineering capacity
• Expertise in writing and maintaining robust data pipelines in SQL and Python
• A track record of turning data requirements from stakeholders into actionable plans
• Experience with cloud-native solutions
• A history of implementing and upholding best practices in data pipelines, ensuring accuracy, consistency, and reliability
• 5+ years of experience in data warehousing, data modelling, and building/maintaining ETL/ELT pipelines at scale.
• Extensive experience with AWS, Google Cloud Platform (GCP), or Azure.
• Proficiency in Apache Spark, Kafka, NoSQL databases, and orchestration tools like Apache Airflow.
• Strong skills in Python, Java, or Scala.
• Deep knowledge of SQL and, specifically, experience with Snowflake, Data bricks, or Redshift.