Senior Data Engineer (AWS Glue + Python) : (NewYork, Austin, Charolette) – Hybrid

Placewell Solution LLC; Austin (TX); Hybrid; USD 110,000 - 140,000; Full time

Job summary

Placewell Solution LLC is seeking a Senior Data Engineer to enhance cloud data pipelines using AWS services. The role requires deep expertise in AWS Glue and Python for developing reliable ETL/ELT processes.

Ideal candidates will have strong data engineering backgrounds with experience in AWS, especially with data quality controls, data modeling, and performance optimization. This position offers a hybrid work model allowing for both remote and onsite collaboration.

Qualifications

• 8+ years of experience in data engineering, with at least 4 years on AWS.
• Strong understanding of orchestration and ETL/ELT processes.
• Ability to collaborate with stakeholders for scalable data solutions.

Responsibilities

• Build and optimize ETL/ELT pipelines using AWS Glue.
• Develop data ingestion and processing with AWS services.
• Implement data quality checks and optimize performance.

Skills

AWS Glue (PySpark)

Python

Data lakes/lakehouse patterns

SQL proficiency

Tools

AWS S3

AWS Glue Catalog

AWS CloudWatch

CI/CD tools (Git, pipelines)

Job description

Title: Senior Data Engineer (AWS Glue + Python) | Type: Contract | Location: Hybrid

Role Summary

We are hiring a Senior Data Engineer to build and optimize cloud data pipelines on AWS, with deep hands‑on expertise in AWS Glue and Python. You will develop reliable ETL/ELT workflows, data quality controls, and scalable data processing patterns.

Key Responsibilities

• Build ETL/ELT pipelines using AWS Glue (PySpark/Python), Glue Workflows, and Glue Catalog
• Develop data ingestion and processing using S3, Lambda, Step Functions, EventBridge
• Implement data modeling and query optimization (Athena/Redshift as applicable)
• Apply data quality checks, lineage/metadata standards, and operational monitoring
• Manage orchestration, retries, alerts, and SLA tracking for pipelines
• Optimize performance and cost (partitioning, compression, job tuning, concurrency)
• Collaborate with stakeholders to translate requirements into scalable data solutions

Required Skills & Experience

• 8+ years overall; 4+ years data engineering on AWS
• Strong Python, Glue (PySpark), and distributed processing concepts
• Strong with S3, Glue Catalog, IAM, CloudWatch
• SQL proficiency and experience with data lakes/lakehouse patterns
• Experience building production‑grade pipelines with CI/CD (Git, pipelines)

Nice to Have

• Iceberg/Hudi/Delta, Lake Formation governance
• Streaming: Kinesis/MSK/Kafka
• Warehouse: Redshift, Snowflake