Mid-Level Analytics Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Mid-Level Analytics Engineer, offering a contract length of "unknown" with a pay rate of "$/hour." Key skills include 2+ years in data engineering, proficiency in SQL and Python, and AWS experience. Snowflake knowledge is preferred.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 20, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Kansas City, MO
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🧠 - Skills detailed
#Deployment #Cloud #"ETL (Extract #Transform #Load)" #Automation #Docker #GIT #SNS (Simple Notification Service) #SQL (Structured Query Language) #S3 (Amazon Simple Storage Service) #Scala #Data Modeling #Python #AWS (Amazon Web Services) #AI (Artificial Intelligence) #ML (Machine Learning) #Batch #Data Engineering #Data Ingestion #SQS (Simple Queue Service) #Snowflake #Monitoring #Schema Design #Normalization #Data Quality #Airflow #Data Pipeline #Lambda (AWS Lambda)
Role description
Key Responsibilities
• Design, build, and maintain event-driven ETL pipelines leveraging AWS services (SNS, SQS, Lambda, ECS, S3) and Snowflake.
• Develop and optimize data ingestion frameworks in Python for both batch and near real-time workloads.
• Partner with the Feature Development team to ensure reliable, scalable data pipelines that enable product features.
• Implement data quality checks, monitoring, and alerting to ensure trustworthy pipelines.
• Optimize queries, schema design, and performance within Snowflake.
• Collaborate with product and engineering teams to understand feature requirements and translate them into robust data solutions.
• Contribute to CI/CD practices and infrastructure-as-code for pipeline deployments.
Must-Have Technical Skills
• 2+ years of professional experience in data engineering or backend development.
• Strong proficiency in SQL (analytical queries, schema design, performance tuning).
• Hands-on experience with Python for ETL and automation.
• Experience with AWS cloud services (SNS, SQS, Lambda, ECS, S3, etc.).
• Knowledge of event-driven or streaming architectures.
• Proficiency with Git workflows and collaborative development.
Preferred Skills
• Experience working with Snowflake at scale (query optimization, task orchestration, warehouse management).
• Familiarity with orchestration tools (Airflow, Dagster, Prefect).
• Understanding of data modeling best practices (star schema, normalization,
• incremental loads).
• Exposure to containerization (Docker) and CI/CD pipelines.
• Interest in AI/ML-driven analytics and semantic layers (Snowflake Cortex)
Key Responsibilities
• Design, build, and maintain event-driven ETL pipelines leveraging AWS services (SNS, SQS, Lambda, ECS, S3) and Snowflake.
• Develop and optimize data ingestion frameworks in Python for both batch and near real-time workloads.
• Partner with the Feature Development team to ensure reliable, scalable data pipelines that enable product features.
• Implement data quality checks, monitoring, and alerting to ensure trustworthy pipelines.
• Optimize queries, schema design, and performance within Snowflake.
• Collaborate with product and engineering teams to understand feature requirements and translate them into robust data solutions.
• Contribute to CI/CD practices and infrastructure-as-code for pipeline deployments.
Must-Have Technical Skills
• 2+ years of professional experience in data engineering or backend development.
• Strong proficiency in SQL (analytical queries, schema design, performance tuning).
• Hands-on experience with Python for ETL and automation.
• Experience with AWS cloud services (SNS, SQS, Lambda, ECS, S3, etc.).
• Knowledge of event-driven or streaming architectures.
• Proficiency with Git workflows and collaborative development.
Preferred Skills
• Experience working with Snowflake at scale (query optimization, task orchestration, warehouse management).
• Familiarity with orchestration tools (Airflow, Dagster, Prefect).
• Understanding of data modeling best practices (star schema, normalization,
• incremental loads).
• Exposure to containerization (Docker) and CI/CD pipelines.
• Interest in AI/ML-driven analytics and semantic layers (Snowflake Cortex)
