Senior Data Engineer Remote

Position Summary:

Eraytec is urgently seeking a highly skilled, results-driven Senior Data Engineer to join our dynamic technology practice in a 100% remote contract capacity. In this high-impact engineering role, you will take ownership of architecting, building, scaling, and optimizing mission-critical big data processing pipelines and cloud analytics infrastructure. Leveraging over six years of specialized experience in advanced Python scripting, PySpark distributed computing, and AWS data ecosystems, you will transform massive volumes of complex data into high-performance analytical assets.

This position offers an outstanding remote opportunity for an ambitious data engineering professional to deliver enterprise-scale solutions, optimize data workflows, and drive impactful business intelligence capabilities across distributed cloud environments.

As a Senior Data Engineer at Eraytec working remotely, you will lead the technical design, development, and ongoing maintenance of high-throughput distributed data pipelines. Collaborating closely with cross-functional agile teams including data architects, business intelligence specialists, machine learning engineers, and cloud infrastructure operations, you will translate complex analytical and enterprise reporting requirements into resilient, automated, and scalable cloud data solutions.

Your core technical mandate centers on engineering petabyte-scale data pipelines using Python and PySpark, writing highly optimized SQL queries to query complex relational and columnar data structures, and orchestrating serverless and managed workflows across Amazon Web Services. You will manage and streamline end-to-end data pipelines leveraging AWS S3 for scalable data lakes, Amazon Redshift for high-performance data warehousing, AWS Lambda for event-driven serverless ingestion, AWS EMR for distributed computing clusters, and AWS Glue for automated metadata cataloging and ETL jobs.

Additionally, you will build and govern robust data orchestration DAGs using Apache Airflow, enforce strict data governance and automated quality checks, and diagnose performance bottlenecks across distributed environments. This contract engagement demands sharp diagnostic problem-solving capabilities, deep expertise in cloud data engineering internals, and the self-discipline to excel within a fast-paced remote work model.

• Design, build, scale, and maintain high-performance batch and real-time big data pipelines utilizing Python, PySpark, and AWS cloud data services.
• Develop complex, highly performant distributed data transformations and processing routines on Amazon EMR clusters.
• Write, tune, and optimize advanced SQL queries, data models, and aggregation schemas across large-scale relational and analytical databases.
• Architect and manage enterprise cloud data lakehouse storage structures utilizing Amazon S3, AWS Glue Data Catalog, and Amazon Redshift.
• Implement and orchestrate automated, fault-tolerant workflow pipelines and directed acyclic graphs (DAGs) using Apache Airflow.
• Deploy event-driven, serverless data ingestion pipelines and micro-batch processors utilizing AWS Lambda functions.
• Identify, troubleshoot, and resolve performance bottlenecks, pipeline failures, and distributed computing memory issues across big data environments.
• Implement automated data validation rules, data quality frameworks, comprehensive monitoring, and end-to-end pipeline alerting mechanisms.

• Minimum 6+ years of dedicated professional experience in big data engineering, data platform development, and distributed data systems.
• Advanced programming and scripting expertise in Python specifically tailored for complex data processing, pipeline automation, and ETL operations.
• Proven hands-on technical proficiency with PySpark for distributed data transformation, Data Frame manipulations, and large-scale data cleansing.
• Demonstrated mastery of SQL, data modeling, database indexing, and query optimization for high-volume big data workloads.
• Extensive hands-on architectural experience across native AWS data services:
  Amazon S3, Amazon Redshift, AWS Lambda, AWS EMR, and AWS Glue.
• Substantial experience designing, deploying, and managing automated workflow orchestration DAGs utilizing Apache Airflow.
• Proven track record of designing resilient data architectures capable of handling large-scale structured and semi-structured datasets.
• Excellent analytical problem-solving…