AWS Data Engineer - ETL, Spark & Redshift Expert

Teksystems; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Teksystems is seeking a data engineer to build cloud-native data platforms in the United States. You will design scalable ETL/ELT pipelines on AWS, ingest data from diverse sources, and organize it in data lakes and data warehouses.

The role requires deep knowledge of AWS services (EC2, S3, RDS, DynamoDB, Redshift, Glue, Lambda, EMR), Python, PySpark, SQL, and big data frameworks like Hadoop and Spark. Collaboration with analytics teams is essential for secure, reliable data architectures.

Qualifications

• In-depth knowledge of AWS services related to data engineering: EC2, S3, RDS, DynamoDB, Redshift, Glue, Lambda, Step Functions, Kinesis, Iceberg,EMR, and Athena.
• Strong understanding of cloud architecture and best practices for high availability and fault tolerance.
• Expertise in ETL/ELT processes, data modeling, and data warehousing.
• Knowledge of data lakes, data warehouses, and big data processing frameworks like Apache Hadoop and Spark.
• Proficiency in Python, Pyspark and SQL for data manipulation and pipeline development.
• Expertise in working with data warehousing solutions like Redshift.

Responsibilities

• Design and implement scalable data pipelines on AWS for ingestion, transformation, and storage.
• Collaborate with data scientists and engineers to optimize data flows and analytics readiness.
• Ensure data quality, lineage, security, and governance across platforms.

Skills

ETL/ELT processes

Data modeling

Data warehousing

Data lakes

Big data processing

Tools

AWS Glue

Amazon EMR

Amazon Redshift

PySpark

Python

SQL

Hadoop

Spark

Job description

Teksystems is seeking a data engineer to build cloud-native data platforms in the United States. You will design scalable ETL/ELT pipelines on AWS, ingest data from diverse sources, and organize it in data lakes and data warehouses.

The role requires deep knowledge of AWS services (EC2, S3, RDS, DynamoDB, Redshift, Glue, Lambda, EMR), Python, PySpark, SQL, and big data frameworks like Hadoop and Spark. Collaboration with analytics teams is essential for secure, reliable data architectures.