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Data Engineer (SQL Server/SSIS + AWS)

Nineteen Eleven SolutionsMemphis, TN🇺🇸United StatesPosted 20 Jul 2026

Why This Role Stands Out

This role offers a fantastic opportunity to contribute to impactful data solutions and explore emerging technologies within a reputable company. You'll thrive here if you possess strong SQL Server, SSIS, and AWS skills and are eager to grow your technical leadership while ensuring data integrity and system performance. Apply now to take your career to the next level!

Quick Overview

Work Type
On Site
Level
Mid Senior

Job Description

Job Title: Data Engineer (SQL Server/SSIS + AWS)
Location: Memphis, TN (Local candidates only)
Duration: 12+ Months Contract 
 
 
A Software/Data Engineer (SQL Server/SSIS + AWS) Engineer, Business Intelligence Data Movement provides technical leadership while actively contributing to the design, development, and support of complex data ingestion pipelines and platform services. This role works across multiple systems to ensure reliable data movement, system performance, and production stability, while contributing to architectural decisions and assisting other engineers. Explores emerging technologies and anticipates industry disruptions. Documents and demonstrates solutions by developing documentation, flowcharts, layouts, diagrams, charts, code comments, and conforms to coding standards.

ESSENTIAL JOB FUNCTIONS

  • Design, build, and maintain data ingestion pipelines and synchronization processes (full load, incremental, and change tracking).
  • Develop and support SQL Server–based and API-based data pipelines to ingest data into centralized data platforms.
  • Troubleshoot and resolve production incidents, including data issues, pipeline failures, and performance bottlenecks.
  • Contribute to system design decisions, including tradeoffs for scalability, performance, reliability, and cost Implement and maintain monitoring, alerting, and validation to ensure data quality and pipeline reliability.
  • Collaborate with cross-functional teams (source systems, data transformation, architecture) to ensure end-to-end data flow success.
  • Participate in code reviews, design reviews, and best practice adoption

REQUIREMENTS

  • .NET-based data processing systems and Python for data engineering use cases
  • Familiarity with AWS data services (S3, Lambda, Glue, Athena, CloudWatch/CloudTrail) and cloud-native pipeline design
  • Experience evaluating architectures against the AWS Well-Architected Framework
  • Data validation, reconciliation, and data quality checks
  • Ability to design monitoring, alerting, and observability for pipelines and data systems
  • Experience with source control and release management workflows (Git/Bitbucket)
  • Familiarity with modern data transformation tools such as DBT (Data Build Tool)
  • Data integration platforms such as CData or similar connector-based ingestion tools
  • Awareness of data governance, lineage, and ownership practices

Technical Skillset

  • Experience with data ingestion and replication patterns (full load, incremental, CDC/change tracking)
  • Hands-on experience building and supporting ETL/ELT pipelines within a data lake or data warehouse ecosystem
  • Experience developing and operating SSIS-based data movement pipelines and similar orchestration tools

Top Skills Required

  • Design, build, and maintain data ingestion pipelines and synchronization processes (full load, incremental, and change tracking)
  • Strong SQL and SQL Server expertise (query tuning, indexing, performance troubleshooting, blocking/locking analysis) and SSIS
  • Familiarity with AWS data services (S3, Lambda, Glue, Athena, CloudWatch/CloudTrail) and cloud-native pipeline design
Please anwere Screening questions: (Add these on top of resume to receive first preference) 
 
Q1: Walk me through how you''d design an SSIS package to handle incremental loads with change tracking, versus a full load. What''s your trigger for choosing one over the other?
Q2: A pipeline that normally runs in 10 minutes is now taking 2 hours. How do you troubleshoot it in SQL Server?
Q3: Give me a specific example of a data pipeline task you built in .NET, and a separate one in Python. What made you choose one language over the other for each?
Q4: Describe a time you used Glue, Lambda, or Athena in a data pipeline. What problem were you solving, and why that service instead of an alternative?
Q5: How would you evaluate a new pipeline design against the AWS Well-Architected Framework? Which pillar tends to get overlooked?
Q6:"How do you validate that a data load completed correctly — not just that it ran without errors, but that the data itself is right?
Q7: Tell me about a pipeline failure you''ve dealt with in production — what broke, how did you find out, and how did you fix it long-term (not just patch it)?
Q8: Have you used DBT? Walk me through a model you built.
Q9: Have you worked with connector tools like CData, Fivetran, or similar? What was the pipeline it powered?
Q10: How do you approach data lineage or documenting where data comes from and who owns it?
 
 

Skills

SQL
SQL Server
AWS
ETL
Data Pipeline
.NET
Git
Python
dbt

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