TechDoQuest
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a 6-month contract, offering a pay rate of "X" per hour. Key skills include Confluent Kafka, AWS S3, Snowflake, and Google Dataflow. Requires 3+ years of data engineering experience and familiarity with automotive service data.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
February 20, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Renton, WA
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🧠 - Skills detailed
#Normalization #Programming #AWS (Amazon Web Services) #API (Application Programming Interface) #S3 (Amazon Simple Storage Service) #Visualization #Data Quality #Data Analysis #Computer Science #Data Architecture #"ETL (Extract #Transform #Load)" #Security #Data Processing #Data Ingestion #Automation #AWS S3 (Amazon Simple Storage Service) #Data Cleansing #Data Security #GCP (Google Cloud Platform) #Storage #Data Pipeline #Snowflake #Dataflow #Tableau #SQL (Structured Query Language) #Python #Kafka (Apache Kafka) #Data Engineering #Documentation #Batch #Data Modeling #Cloud #Compliance
Role description
Key Responsibilities
Data Ingestion & Integration
Develop and maintain data ingestion pipelines for service and repair data using Confluent Kafka for event streaming.
Implement connectors and integrations between Kafka, AWS S3, Google Dataflow, and Snowflake to facilitate batch and real-time data flows.
Work with APIs and Apigee to securely ingest and distribute data across internal and external systems, including dealer networks.
Data Cleansing & Transformation
Build and optimize data cleansing, normalization, and transformation pipelines in Google Dataflow for real-time processing.
Design and implement batch transformation jobs within Snowflake, building and maintaining the Operational Data Store (ODS).
Ensure data quality, consistency, and integrity across all processing stages.
Data Publishing & Reporting Support
Publish transformed and aggregated data to internal and external dashboards using APIs, Kafka topics, and Tableau.
Collaborate with data analysts and business stakeholders to support reporting and analytics requirements.
Monitor and troubleshoot data pipelines to ensure high availability and performance.
Collaboration & Documentation
Partner with data architects, analysts, and external dealer teams to understand data requirements and source systems.
Document data workflows, processing logic, and integration specifications.
Adhere to best practices in data security, governance, and compliance.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Required Technologies & Skills
Event Streaming: Confluent Kafka (proficiency), Kafka Connectors
API Management: Apigee(proficiency)
Cloud Storage & Data Warehousing: AWS S3, Snowflake
Data Processing: Google Dataflow
Programming: SQL, Python (proficiency)
Batch & Real-Time Pipeline Development
Data Visualization Support: Tableau (basic understanding for data publishing)
Experience building Operational Data Stores (ODS) and data transformation pipelines in Snowflake
Familiarity with truck industry aftersales or automotive service and repair data is a plus
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
3+ years of proven experience in data engineering, especially with streaming and batch data pipelines.
Hands-on experience with Kafka ecosystem (Confluent Kafka, Connectors) and cloud data platforms (Snowflake, AWS).
Skilled in Python programming for data processing and automation.
Experience with Google Cloud Platform services, especially Google Dataflow, is highly desirable.
Strong understanding of data modeling, ETL/ELT processes, and data quality principles.
Ability to work collaboratively in cross-functional teams and communicate technical concepts effectively.
Automotive domain
Key Responsibilities
Data Ingestion & Integration
Develop and maintain data ingestion pipelines for service and repair data using Confluent Kafka for event streaming.
Implement connectors and integrations between Kafka, AWS S3, Google Dataflow, and Snowflake to facilitate batch and real-time data flows.
Work with APIs and Apigee to securely ingest and distribute data across internal and external systems, including dealer networks.
Data Cleansing & Transformation
Build and optimize data cleansing, normalization, and transformation pipelines in Google Dataflow for real-time processing.
Design and implement batch transformation jobs within Snowflake, building and maintaining the Operational Data Store (ODS).
Ensure data quality, consistency, and integrity across all processing stages.
Data Publishing & Reporting Support
Publish transformed and aggregated data to internal and external dashboards using APIs, Kafka topics, and Tableau.
Collaborate with data analysts and business stakeholders to support reporting and analytics requirements.
Monitor and troubleshoot data pipelines to ensure high availability and performance.
Collaboration & Documentation
Partner with data architects, analysts, and external dealer teams to understand data requirements and source systems.
Document data workflows, processing logic, and integration specifications.
Adhere to best practices in data security, governance, and compliance.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Required Technologies & Skills
Event Streaming: Confluent Kafka (proficiency), Kafka Connectors
API Management: Apigee(proficiency)
Cloud Storage & Data Warehousing: AWS S3, Snowflake
Data Processing: Google Dataflow
Programming: SQL, Python (proficiency)
Batch & Real-Time Pipeline Development
Data Visualization Support: Tableau (basic understanding for data publishing)
Experience building Operational Data Stores (ODS) and data transformation pipelines in Snowflake
Familiarity with truck industry aftersales or automotive service and repair data is a plus
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.
3+ years of proven experience in data engineering, especially with streaming and batch data pipelines.
Hands-on experience with Kafka ecosystem (Confluent Kafka, Connectors) and cloud data platforms (Snowflake, AWS).
Skilled in Python programming for data processing and automation.
Experience with Google Cloud Platform services, especially Google Dataflow, is highly desirable.
Strong understanding of data modeling, ETL/ELT processes, and data quality principles.
Ability to work collaboratively in cross-functional teams and communicate technical concepts effectively.
Automotive domain
