SoftStandard Solutions
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Engineer with 7–9 years of experience, focusing on building scalable data pipelines. Contract length is W2, onsite work is required, and expertise in AWS, GCP, or Azure is essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
July 2, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Version Control #Airflow #Distributed Computing #GitHub #Apache Airflow #Data Architecture #Databricks #Data Lake #ADLS (Azure Data Lake Storage) #GCP (Google Cloud Platform) #Jenkins #Security #Apache Kafka #Data Processing #Kafka (Apache Kafka) #Python #Dataflow #BigQuery #Data Governance #Scala #Data Warehouse #Business Analysis #GIT #Observability #AWS (Amazon Web Services) #Terraform #SQL (Structured Query Language) #Batch #Computer Science #ML (Machine Learning) #Data Pipeline #Redshift #Spark (Apache Spark) #Azure #Cloud #Snowflake #Delta Lake #"ETL (Extract #Transform #Load)" #Mathematics #dbt (data build tool) #Kubernetes #DevOps #Synapse #Apache Spark #Docker #S3 (Amazon Simple Storage Service) #Data Science #Data Quality #Containers #Data Engineering
Role description
Senior Sr. Data Engineer
Experience: 7–9 Years
Onsite: As per the client's preference
Contract: W2
Roles & Responsibilities
• Design, build, and maintain scalable and reliable data pipelines for batch and real-time data processing
• Architect and manage modern data lake, data warehouse, and lakehouse solutions across cloud platforms
• Develop and maintain ETL/ELT workflows to ingest, transform, and deliver data to downstream consumers
• Optimize data models, schemas, and query performance for large-scale analytical workloads
• Collaborate with data scientists, ML engineers, and business analysts to deliver clean, reliable data products
• Implement data quality checks, validation frameworks, and observability tooling across pipelines
• Build and maintain streaming data solutions using Kafka, Spark Streaming, or Flink
• Enforce data governance, lineage tracking, and security best practices across all data assets
• Support CI/CD for data pipelines and infrastructure using modern DevOps practices
Tools & Technologies
• Languages: Python, SQL, Scala (preferred)
• Pipeline / ETL: Apache Spark, Apache Airflow, dbt, Glue, Dataflow
• Streaming: Apache Kafka, Spark Streaming, Apache Flink
• Cloud Platforms: AWS (Redshift, S3, Glue, EMR), GCP (BigQuery, Dataflow, Pub/Sub), Azure (Synapse, Data Factory, ADLS)
• Data Warehouse / Lakehouse: Snowflake, Databricks, Delta Lake, BigQuery, Redshift
• Orchestration: Apache Airflow, Prefect, Dagster
• Containers: Docker, Kubernetes
• Version Control & CI/CD: Git, GitHub Actions, Jenkins, Terraform
• Data Quality: Great Expectations, dbt tests, Monte Carlo
Requirements
• 7–9 years of overall experience in data engineering or software engineering
• 4+ years of hands-on experience designing and maintaining production-grade data pipelines
• Strong expertise in at least one major cloud platform — AWS, GCP, or Azure
• Deep knowledge of distributed computing and large-scale data processing using Spark
• Experience with both batch and real-time streaming data architectures
• Degree in Computer Science, Engineering, Mathematics, or equivalent practical experience
Senior Sr. Data Engineer
Experience: 7–9 Years
Onsite: As per the client's preference
Contract: W2
Roles & Responsibilities
• Design, build, and maintain scalable and reliable data pipelines for batch and real-time data processing
• Architect and manage modern data lake, data warehouse, and lakehouse solutions across cloud platforms
• Develop and maintain ETL/ELT workflows to ingest, transform, and deliver data to downstream consumers
• Optimize data models, schemas, and query performance for large-scale analytical workloads
• Collaborate with data scientists, ML engineers, and business analysts to deliver clean, reliable data products
• Implement data quality checks, validation frameworks, and observability tooling across pipelines
• Build and maintain streaming data solutions using Kafka, Spark Streaming, or Flink
• Enforce data governance, lineage tracking, and security best practices across all data assets
• Support CI/CD for data pipelines and infrastructure using modern DevOps practices
Tools & Technologies
• Languages: Python, SQL, Scala (preferred)
• Pipeline / ETL: Apache Spark, Apache Airflow, dbt, Glue, Dataflow
• Streaming: Apache Kafka, Spark Streaming, Apache Flink
• Cloud Platforms: AWS (Redshift, S3, Glue, EMR), GCP (BigQuery, Dataflow, Pub/Sub), Azure (Synapse, Data Factory, ADLS)
• Data Warehouse / Lakehouse: Snowflake, Databricks, Delta Lake, BigQuery, Redshift
• Orchestration: Apache Airflow, Prefect, Dagster
• Containers: Docker, Kubernetes
• Version Control & CI/CD: Git, GitHub Actions, Jenkins, Terraform
• Data Quality: Great Expectations, dbt tests, Monte Carlo
Requirements
• 7–9 years of overall experience in data engineering or software engineering
• 4+ years of hands-on experience designing and maintaining production-grade data pipelines
• Strong expertise in at least one major cloud platform — AWS, GCP, or Azure
• Deep knowledge of distributed computing and large-scale data processing using Spark
• Experience with both batch and real-time streaming data architectures
• Degree in Computer Science, Engineering, Mathematics, or equivalent practical experience
