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# Data Engineer

- **Company:** TUNDRA TECHNICAL
- **Location:** Arlington, VA, United States
- **Experience:** Expert
- **Salary:** $146,328.0 - $165,838.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Business Analytics Applications, Big Data, Databases, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Mart, Data Mining, Data Structures, Data Systems, Data Warehousing, Database Queries, Dimensional Modeling, Python (Programming Language), Performance Tuning, Scrum Methodology, Query Optimization, Power BI, Cloud Services, Microsoft Copilot, Software Engineering, SQL Databases, Tableau (Software), Technical Data Management Systems, Data Processing, Scripting, Sql Optimization, Boto3, Git, Pandas, Information Technology, Amazon Bedrock, Data Analytics, Star Schema, Machine Learning Operations, Data Pipelines, Amazon Redshift
- **Published:** October 2, 2026
- **Apply:** https://www.disabledperson.com/jobs/75686237-data-engineer

## About the Role

* 5+ years of experience in data analytics, data engineering, or business intelligence
 * Expert-level SQL proficiency - complex joins, window functions, CTEs, subqueries, query optimization, and performance tuning in Amazon Redshift or similar MPP databases
 * Demonstrated experience conducting RCA, implementing fixes, and building enhancements on data pipelines, reports, and analytics systems
 * Proven ability to convert business requirements into technical data solutions - pipeline logic, SQL transformations, report builds, and data model changes
 * Deep experience creating dashboards, reports, analyses, and datasets using BI tools - Amazon QuickSight strongly preferred; Tableau, Power BI, or similar also acceptable
 * Proven ability to work with business stakeholders to define metrics and KPIs, and drive the build of reporting solutions
 * Strong understanding of data integration and ETL/ELT concepts - extraction patterns, transformation logic, load strategies, incremental vs. full refresh, CDC, and pipeline architecture
 * Deep knowledge of data warehouse and data mart design - star schema, snowflake schema, fact/dimension tables, slowly changing dimensions, and aggregation layers
 * Hands-on experience with Apache Airflow - ability to read DAG code, understand task dependencies, troubleshoot failures, and drive fixes with engineering teams
 * Strong Python programming skills (pandas, boto3, SQL libraries, automation scripting)
 * Working knowledge of AWS cloud services (Redshift, S3, Glue, Athena, Lambda)
 * Track record of collaborating with engineering teams to drive data requirements and solution design, * Experience building and managing QuickSight datasets, calculated fields, parameters, and SPICE-optimized data models
 * Experience with data quality frameworks, data validation, and data governance
 * Experience with Smartsheets for automation and reporting workflows
 * Hands-on experience with AI/ML tools (e.g., Amazon Bedrock, Amazon Q, Copilot, or similar)
 * Familiarity with Git version control and CI/CD for data pipelines
 * Experience with Agile/Scrum methodologies
 * Bachelor's degree in Computer Science, Data Science, Engineering, or related field

## Description

Please note that this posting is for a contract assignment and is not a full-time employment opportunity with Amazon. Candidates selected for roles will be employed as contract workers through Amazon's approved 3rd party Direct Source provider for the duration of the specified project and will not be an employee of Amazon.

We are seeking a technically strong Data & Analytics Engineer with deep expertise in SQL, data integration, data warehousing, and reporting. In this role, you will conduct root cause analysis on data and pipeline issues, implement fixes, build enhancements to existing systems, and convert business requirements into scalable data solutions. You will leverage hands-on experience with Redshift, Airflow, ETL patterns, and BI tools to drive continuous improvement across the analytics ecosystem.

This is a technically focused, hands-on role for a data professional who excels at diagnosing issues, fixing them, and building better solutions. You will spend your time conducting RCA on data and reporting problems, implementing fixes, building enhancements that improve the analytics ecosystem, and converting business needs into working data solutions. You are equally comfortable troubleshooting a broken Airflow DAG as you are building a new QuickSight dashboard from a stakeholder's requirements., * Conduct Root Cause Analysis (RCA) on data quality issues, pipeline failures, reporting discrepancies, and business anomalies - and implement fixes to resolve them

 * Build enhancements to existing data pipelines, reports, metrics, and analytics solutions to improve performance, accuracy, and coverage
 * Convert business requirements into technical data solutions - translating stakeholder needs into SQL logic, pipeline changes, report designs, and data model updates
 * Work with business stakeholders to define metrics and KPIs, and drive the build of measurement frameworks that track business outcomes and operational health
 * Create dashboards, reports, analyses, and datasets - preferably in Amazon QuickSight - to deliver actionable insights to stakeholders at all levels
 * Write advanced SQL in Amazon Redshift for complex data extraction, transformation, analysis, and performance optimization across large-scale datasets
 * Leverage deep experience with ETL/ELT concepts and data integration patterns to drive solutioning and partner with tech teams on pipeline fixes and enhancements
 * Apply strong knowledge of data warehouse architecture and data mart design (star schema, snowflake schema, dimensional modeling) to quickly understand organizational data structures and deliver quality analysis
 * Read, interpret, and troubleshoot Apache Airflow DAGs to understand data pipeline orchestration, identify root causes, and drive fixes with engineering teams
 * Develop Python-based automation scripts and data processing workflows to streamline reporting and analytics
 * Utilize AWS cloud infrastructure (Redshift, S3, Glue, Lambda, Athena, EMR) to support and enhance data solutions
 * Create and manage automation workflows using Smartsheets for operational reporting and tracking
 * Partner with software development and data engineering teams to drive requirements, evaluate technical approaches, and implement the right solution
 * Utilize AI/ML tools and generative AI to enhance data processing and automate repetitive tasks


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