AWS Senior Data Lead

Inherent Technologies; Seattle (WA); On-site; USD 140,000 - 180,000; Full time

Job summary

Inherent Technologies seeks an AWS Senior Data Lead to architect and drive data platform initiatives in Seattle. This onsite, year-long engagement focuses on building scalable data pipelines, data lakes, and real-time processing in AWS.

You will collaborate with data teams and business stakeholders to translate analytics needs into robust, secure, and performant solutions while mentoring engineers and shaping data governance.

Qualifications

• 7+ years of experience in Data Engineering and Data Integration.
• Hands-on experience with AWS-based data platforms and cloud-native applications.
• Strong expertise in Python and PySpark development.
• Experience implementing CDC and real-time data streaming solutions.
• Strong understanding of data modeling, SQL optimization, and enterprise data architecture.
• Excellent communication and stakeholder management skills.

Responsibilities

• Design, develop, and maintain scalable data pipelines in AWS using Glue, Python, and PySpark.
• Build robust data ingestion frameworks to collect and process data from multiple on-premises and cloud-based sources.
• Implement CDC for near real-time data synchronization.
• Utilize AWS DMS to migrate and replicate data across enterprise systems with minimal downtime.
• Design and implement event-driven architectures using Kafka, Amazon SNS, Amazon SQS, and EventBridge.
• Develop cloud-native data processing solutions supporting high availability, scalability, and performance.
• Build, optimize, and maintain enterprise Data Lake solutions.
• Monitor and troubleshoot data workflows using AWS CloudWatch and AWS CloudTrail.
• Develop efficient Python-based data processing applications leveraging libraries such as Pandas and NumPy.
• Work with relational and NoSQL databases including SQL Server, DynamoDB, MongoDB, and Cassandra.
• Support migration and modernization of legacy data pipelines into AWS-based architectures.
• Collaborate with business stakeholders, application owners, and data teams to understand data architecture, business requirements, and analytics needs.
• Document data models, ETL processes, data flows, and target-state architectures.
• Drive continuous improvements in data quality, platform performance, security, and operational efficiency.

Skills

Data engineering

AWS

Python

PySpark

CDC

SQL

Communication

Tools

AWS Glue

AWS DMS

Kafka

SNS/SQS

EventBridge

CloudWatch

Job description

Position: AWS Senior Data Lead

Location: Seattle, WA ***Onsite***

Duration: 1 Year

Key Responsibilities

• Design, develop, and maintain scalable data pipelines in AWS using AWS Glue, Python, and PySpark.
• Build robust data ingestion frameworks to collect and process data from multiple on-premises and cloud-based sources.
• Implement Change Data Capture (CDC) solutions for near real-time data synchronization and replication.
• Utilize AWS Database Migration Service (AWS DMS) to migrate and replicate data across enterprise systems with minimal downtime.
• Design and implement event-driven architectures using Kafka, Amazon SNS, Amazon SQS, and EventBridge.
• Develop cloud-native data processing solutions supporting high availability, scalability, and performance.
• Build, optimize, and maintain enterprise Data Lake solutions.
• Monitor and troubleshoot data workflows using AWS CloudWatch and AWS CloudTrail.
• Develop efficient Python-based data processing applications leveraging libraries such as Pandas and NumPy.
• Work with relational and NoSQL databases including SQL Server, DynamoDB, MongoDB, and Cassandra.
• Support migration and modernization of legacy data pipelines into AWS-based architectures.
• Collaborate with business stakeholders, application owners, and data teams to understand data architecture, business requirements, and analytics needs.
• Document data models, ETL processes, data flows, and target-state architectures.
• Drive continuous improvements in data quality, platform performance, security, and operational efficiency.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines in AWS using AWS Glue, Python, and PySpark.
• Build robust data ingestion frameworks to collect and process data from multiple on-premises and cloud-based sources.
• Implement Change Data Capture (CDC) solutions for near real-time data synchronization and replication.
• Utilize AWS Database Migration Service (AWS DMS) to migrate and replicate data across enterprise systems with minimal downtime.
• Design and implement event-driven architectures using Kafka, Amazon SNS, Amazon SQS, and EventBridge.
• Develop cloud-native data processing solutions supporting high availability, scalability, and performance.
• Build, optimize, and maintain enterprise Data Lake solutions.
• Monitor and troubleshoot data workflows using AWS CloudWatch and AWS CloudTrail.
• Develop efficient Python-based data processing applications leveraging libraries such as Pandas and NumPy.
• Work with relational and NoSQL databases including SQL Server, DynamoDB, MongoDB, and Cassandra.
• Support migration and modernization of legacy data pipelines into AWS-based architectures.
• Collaborate with business stakeholders, application owners, and data teams to understand data architecture, business requirements, and analytics needs.
• Document data models, ETL processes, data flows, and target-state architectures.
• Drive continuous improvements in data quality, platform performance, security, and operational efficiency.

Qualification

• 7+ years of experience in Data Engineering and Data Integration.
• Hands-on experience with AWS-based data platforms and cloud-native applications.
• Strong expertise in Python and PySpark development.
• Experience implementing CDC and real-time data streaming solutions.
• Strong understanding of data modeling, SQL optimization, and enterprise data architecture.
• Excellent communication and stakeholder management skills.