IntePros
Sr Data Engineer
IntePros is seeking a Data Engineer II with a strong foundation in SQL, Python, and AWS to join a team focused on building robust, scalable, and high-performance data systems. This role will specialize in managing time-series data using Amazon Timestream, optimizing complex data workflows, and collaborating closely with engineering and data science stakeholders to ensure the reliability and performance of cloud-native pipelines.
This is an exciting opportunity for a technically strong and curious data engineer to work on cutting-edge infrastructure that supports real-time analytics, automation, and scalable data ingestion across a rapidly evolving cloud environment.
Key Responsibilities Design, implement, and optimize complex SQL queries and time-series database schemas. Build and manage cloud-native data pipelines across AWS services including
Lambda, Redshift, and S3 . Develop Python-based automation and processing logic for real-time and batch data ingestion. Define data retention policies, performance optimizations, and monitoring strategies for time-series datasets. Work cross-functionally with developers and technical teams to troubleshoot issues and scale systems. Apply best practices in cloud-based data architecture, backup, disaster recovery, and performance tuning. Participate in strategic planning for data growth, analytics enablement, and system resilience. Top 3 Must-Have Skills SQL Mastery
- Complex querying, schema design, and performance optimization (4-5 years). Python Development
- For data processing, AWS integration, and automation. AWS Cloud Services
- Strong experience with S3, Redshift, Lambda, and especially Amazon Timestream. Skills & Experience Required 4-5 years of experience in data engineering or infrastructure-focused roles. Hands-on experience with
Amazon Timestream
or other time-series databases. Proficiency with Python and libraries such as Pandas, SQLAlchemy, or PySpark. Expertise in cloud data management, pipeline performance tuning, and automation in AWS. Strong troubleshooting and problem-solving skills for high-scale environments. Understanding of NoSQL and relational database performance management and disaster recovery. Bachelor's degree in Computer Science, Data Engineering, or a related field. Preferred Qualifications Experience scaling cloud-based infrastructure for real-time analytics. Familiarity with performance metrics related to infrastructure operations or time-series data. Exposure to DevOps principles and data observability tools. #LI-ES1
Key Responsibilities Design, implement, and optimize complex SQL queries and time-series database schemas. Build and manage cloud-native data pipelines across AWS services including
Lambda, Redshift, and S3 . Develop Python-based automation and processing logic for real-time and batch data ingestion. Define data retention policies, performance optimizations, and monitoring strategies for time-series datasets. Work cross-functionally with developers and technical teams to troubleshoot issues and scale systems. Apply best practices in cloud-based data architecture, backup, disaster recovery, and performance tuning. Participate in strategic planning for data growth, analytics enablement, and system resilience. Top 3 Must-Have Skills SQL Mastery
- Complex querying, schema design, and performance optimization (4-5 years). Python Development
- For data processing, AWS integration, and automation. AWS Cloud Services
- Strong experience with S3, Redshift, Lambda, and especially Amazon Timestream. Skills & Experience Required 4-5 years of experience in data engineering or infrastructure-focused roles. Hands-on experience with
Amazon Timestream
or other time-series databases. Proficiency with Python and libraries such as Pandas, SQLAlchemy, or PySpark. Expertise in cloud data management, pipeline performance tuning, and automation in AWS. Strong troubleshooting and problem-solving skills for high-scale environments. Understanding of NoSQL and relational database performance management and disaster recovery. Bachelor's degree in Computer Science, Data Engineering, or a related field. Preferred Qualifications Experience scaling cloud-based infrastructure for real-time analytics. Familiarity with performance metrics related to infrastructure operations or time-series data. Exposure to DevOps principles and data observability tools. #LI-ES1