Techvy Corp
Java Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Java Data Engineer in Phoenix, AZ, with a contract length of "X months" and a pay rate of "$X/hour." Requires 5–10 years of experience, strong Java and Apache Spark skills, and familiarity with ETL processes and big data ecosystems.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
February 12, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
On-site
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
Phoenix, AZ
-
🧠 - Skills detailed
#Azure #PostgreSQL #Python #Data Lake #Azure Data Factory #SQL (Structured Query Language) #DevOps #AWS (Amazon Web Services) #Databases #Apache Spark #"ETL (Extract #Transform #Load)" #Jenkins #Data Integration #Spark (Apache Spark) #SQL Server #ML (Machine Learning) #Kafka (Apache Kafka) #Spring Boot #Data Architecture #Snowflake #ADF (Azure Data Factory) #Data Engineering #Synapse #Delta Lake #Batch #Java #Kubernetes #Apache Kafka #Big Data #Docker #Microservices #Databricks #GIT #Data Ingestion #AI (Artificial Intelligence) #Scala #Programming #Hadoop #AWS Glue #Data Pipeline
Role description
Job Title: Java Data Engineer
Location: Phoenix, AZ
Job Overview
We are seeking a Java Data Engineer with strong experience in building scalable data pipelines, integrating real-time streaming systems, and optimizing data workflows for analytics and AI use cases. The ideal candidate will combine Java development expertise with a deep understanding of data engineering, ETL, and big data ecosystems (e.g., Spark, Hadoop, Kafka, Snowflake, or Azure).
Key Responsibilities
• Design and develop data ingestion and transformation pipelines using Java, Spark, and SQL.
• Work with streaming frameworks such as Apache Kafka or Event Hubs to process real-time data.
• Build and maintain ETL/ELT workflows that integrate structured and unstructured data sources.
• Collaborate with data architects and analysts to optimize data models for analytics and reporting.
• Develop and deploy microservices and APIs for data integration and consumption.
• Implement data validation, lineage tracking, and governance standards across pipelines.
• Optimize performance and reliability of distributed systems and batch/streaming jobs.
• Collaborate with DevOps teams to automate CI/CD pipelines for data workflows.
Required Skills and Experience
• 5–10 years of experience in software or data engineering.
• Strong programming skills in Java 8+ (Spring Boot preferred).
• Experience with Apache Spark (Core, SQL, or Structured Streaming).
• Solid understanding of SQL and relational databases (PostgreSQL, SQL Server, etc.).
• Hands-on experience with Kafka, Hadoop, Azure Data Factory, or AWS Glue.
• Familiarity with data lake / lakehouse architectures (Delta Lake, Iceberg, etc.).
• Proficient in writing optimized, reusable, and testable code.
• Understanding of CI/CD, Git, Jenkins, Docker, and Kubernetes.
Nice to Have
• Experience with Python or Scala for data engineering tasks.
• Exposure to Snowflake, Databricks, or Azure Synapse.
• Knowledge of Machine Learning model pipelines and MLOps workflows.
• Strong problem-solving and analytical mindset.
Job Title: Java Data Engineer
Location: Phoenix, AZ
Job Overview
We are seeking a Java Data Engineer with strong experience in building scalable data pipelines, integrating real-time streaming systems, and optimizing data workflows for analytics and AI use cases. The ideal candidate will combine Java development expertise with a deep understanding of data engineering, ETL, and big data ecosystems (e.g., Spark, Hadoop, Kafka, Snowflake, or Azure).
Key Responsibilities
• Design and develop data ingestion and transformation pipelines using Java, Spark, and SQL.
• Work with streaming frameworks such as Apache Kafka or Event Hubs to process real-time data.
• Build and maintain ETL/ELT workflows that integrate structured and unstructured data sources.
• Collaborate with data architects and analysts to optimize data models for analytics and reporting.
• Develop and deploy microservices and APIs for data integration and consumption.
• Implement data validation, lineage tracking, and governance standards across pipelines.
• Optimize performance and reliability of distributed systems and batch/streaming jobs.
• Collaborate with DevOps teams to automate CI/CD pipelines for data workflows.
Required Skills and Experience
• 5–10 years of experience in software or data engineering.
• Strong programming skills in Java 8+ (Spring Boot preferred).
• Experience with Apache Spark (Core, SQL, or Structured Streaming).
• Solid understanding of SQL and relational databases (PostgreSQL, SQL Server, etc.).
• Hands-on experience with Kafka, Hadoop, Azure Data Factory, or AWS Glue.
• Familiarity with data lake / lakehouse architectures (Delta Lake, Iceberg, etc.).
• Proficient in writing optimized, reusable, and testable code.
• Understanding of CI/CD, Git, Jenkins, Docker, and Kubernetes.
Nice to Have
• Experience with Python or Scala for data engineering tasks.
• Exposure to Snowflake, Databricks, or Azure Synapse.
• Knowledge of Machine Learning model pipelines and MLOps workflows.
• Strong problem-solving and analytical mindset.
