Senior AWS Data Engineer- Remote

HonorVet Technologies; Harrisburg (Dauphin County); Remote; USD 110,000 - 170,000; Full time

Job summary

HonorVet Technologies is seeking an experienced Senior AWS Data Engineer to design, build, and maintain scalable data pipelines and cloud-based analytics platforms. The role emphasizes AWS data services, Python, SQL, and big data technologies, handling large, confidential datasets to deliver actionable insights for operations and strategy.

You will collaborate with data scientists and analysts to develop dashboards, models, and decision-support tools, while improving pipeline reliability and

Qualifications

• 5+ years of hands-on data engineering with cloud data services (AWS preferred).
• Strong Python, SQL, and data integration experience.
• Experience designing scalable data pipelines and data warehouses.
• Knowledge of ETL/ELT, data governance, and metadata management.

Responsibilities

• Design, develop, and maintain scalable data pipelines and cloud data architectures.
• Build data solutions using AWS services (S3, Glue, Redshift, EMR, Athena, Lambda).
• Develop and manage workflows with Airflow or similar tools.
• Transform and integrate large datasets using SQL and big data tools.
• Create dashboards, models, and analytics to enable business decisions.
• Collaborate with data scientists and analysts to deliver insights.

Skills

Python

SQL

AWS

Big data

ETL/ELT

Data modeling

Tools

Apache Airflow

AWS Glue

Redshift

EMR

Athena

Lambda

Spark

Kafka

Job description

HonorVet Technologies is a Service Disable Veteran-Owned IT staffing firm, ISO 9001 and ISO 27001 certified, working with federal agencies, state governments, and Fortune 500 enterprise clients across the US. What makes us different isn't a tagline; it's the way we work. We don't forward resumes and hope for the best. We take the time to understand where a professional like you is headed and only reach out when we genuinely believe there's a fit worth exploring.

Job Title: Senior AWS Data Engineer

Location: Harrisburg PA-Remote

Duration: 2+ Months with extension

Work Arrangement: Remote

Interview Mode: Two rounds of interviews, the first via Teams. Second-round interviews for finalists will be in-person in Harrisburg.

Job Description

We are seeking an experienced AWS Data Engineer/ Data Engineer & Insights Analyst to design, develop, and maintain scalable data pipelines, cloud-based data solutions, and analytics platforms. The ideal candidate will have strong expertise in AWS data services, Python, SQL, big data technologies, and data integration. The candidate will be responsible for transforming complex datasets into reliable, actionable insights that support business operations, performance measurement, and strategic decision-making. This role requires hands-on data engineering capabilities, analytical skills, and experience working with large, complex, and confidential datasets.

Responsibilities

• Design, develop, and maintain scalable data pipelines and cloud-based data architectures.
• Build and optimize data solutions using AWS services such as S3, Glue, Redshift, EMR, Athena, Lambda, and related technologies.
• Develop and manage data orchestration workflows using Apache Airflow, AWS Step Functions, or similar tools.
• Extract, transform, integrate, and process large datasets from multiple sources using SQL and big data technologies.
• Develop and maintain ETL/ELT pipelines and data integration processes.
• Implement data quality checks, validation, monitoring, logging, and alerting frameworks.
• Design and optimize data models, data structures, metadata, and data warehouse solutions.
• Work with Python, R, Java, Scala, or other programming languages to develop data processing solutions.
• Build analytical datasets and environments supporting statistical analysis, predictive modeling, and machine learning.
• Develop dashboards, reports, analytical models, and decision-support tools.
• Collaborate with data scientists, business analysts, and cross-functional teams to deliver actionable insights.
• Identify opportunities to automate processes and improve data pipeline performance, scalability, and reliability.
• Support data migration and conversion activities associated with system modernization initiatives.
• Implement data governance, data lineage, security, and privacy controls.
• Integrate data using REST APIs, messaging systems, and stream-processing technologies.

Required Qualifications

• 5+ years of hands-on experience with cloud-based data services, preferably AWS.
• Strong programming experience in Python, R, Java, C++, or Scala.
• Advanced SQL skills and experience with relational and NoSQL databases.
• Experience designing, developing, and optimizing complex data pipelines and architectures.
• Strong experience with ETL/ELT processes and data integration.
• Knowledge of big data technologies such as Hadoop, Spark, and Kafka.
• Experience with workflow orchestration tools such as Apache Airflow, Luigi, or Azkaban.
• Knowledge of data modeling techniques, including dimensional modeling, Data Vault, and Star/Snowflake schemas.
• Experience with Git, version control, and CI/CD pipelines.
• Familiarity with data cataloging and metadata management tools, such as AWS Glue Data Catalog.
• Understanding of data governance, data lineage, and data quality frameworks.
• Experience with REST API design and integration patterns.
• Knowledge of stream-processing technologies such as Spark Streaming and Storm.
• Experience developing dashboards, reports, analytical models, and decision-support solutions.
• Strong analytical, problem-solving, project management, and organizational skills.
• Ability to work independently and collaborate effectively with cross-functional teams.

Preferred Qualifications

• Experience working with higher education, student-level, employment, or workforce datasets.
• Experience integrating data from multiple sources to support analytical and research initiatives.
• Exposure to public-sector data environments, data modernization, and data migration projects.
• Experience supporting statistical analysis, predictive modeling, and machine learning initiatives.