enterprise data engineer

RH Aero Systems

<div><div><div><b>About RH Aero Systems:</b></div></div><div><div>
RH Aero Systems is setting the standard for aviation support equipment and services. Through our industry-leading businesses &ndash; Rhinestahl and HYDRO Systems &ndash; we deliver capability across custom-designed Ground Support Equipment, OEM-licensed engine and airframe tooling, 26 global service centers and innovative engineered solutions for OEMs, MROs and Operators worldwide. RH Aero Systems&rsquo; global headquarters are in Mason, Ohio, USA, with Regional Headquarters in Biberach, Germany and Singapore. For further details visit RHAero.com.</div></div><p>
</p><div><div><b>Job Summary:</b></div></div><p>
</p><div><div>The Data Engineer is responsible for building, maintaining, and scaling RH Aero&rsquo;s enterprise Azure data platform and Microsoft Fabric environment in support of business integration, data consolidation, and analytics transformation initiatives.</div></div><p>
</p><div><div>This role will be central to unifying data across two legacy operating companies as we implement our future ERP systems. The Data Engineer will design, build, and support the pipelines, data structures, quality controls, security model, and semantic layers needed to deliver trusted executive KPI reporting, cross-functional analytics, and future AI-enabled business insights.
</div></div><div></div><div><div>The environment includes multiple enterprise business platforms across manufacturing, service, supply chain, sales, and finance. This is not a report developer role. This is a platform building role for a technically strong engineer who can work across systems, business functions, and data domains.
</div></div><p></p><div><div><b>Why join this role:</b></div></div><ul><li><div>
Opportunity to help build a single, trusted enterprise data foundation across the organization.
</div></li><li><div>Play a key role in transforming fragmented reporting processes into a scalable and governed data platform.
</div></li><li><div>Contribute to improving executive decision-making, self-service analytics, and future AI-enabled insights.</div></li></ul><p>
</p><div><div><b>Key Responsibilities:</b></div></div><p>
</p><div><div>Build RH Aero&rsquo;s Future Analytics Platform
</div></div><ul><li><div>Design and implement the enterprise data platform architecture using Azure and Microsoft Fabric, including data ingestion, storage, transformation, modeling, and governed delivery patterns.
</div></li><li><div>Build and maintain robust automated pipelines from business applications, supplier/customer portals, and operational databases into a unified data environment.
</div></li><li><div>Establish raw, curated, and business-ready data layers to support repeatable analytics, executive dashboards, and self-service reporting.
</div></li><li><div>Integrate and normalize data from Epicor, JobBOSS, SAP ECC, Salesforce, Azure SQL, and other internal and external business systems.
</div></li><li><div>Create reusable data models, dimensions, facts, and business logic to support quote-to-cash, procure-to-pay, inventory, manufacturing, service, finance, and KPI reporting.
</div></li><li><div>Implement data quality checks, exception handling, reconciliation routines, and monitoring to improve trust in reporting and reduce manual work.
</div></li><li><div>Support Power BI delivery through governed semantic models, dataset optimization, security configuration, and drill-through to transaction-level source data.
</div></li><li><div>Help define and implement role-based access, row-level security, object-level security, and workspace governance aligned to company policy and regional privacy requirements.
</div></li><li><div>Document source-to-target mappings, lineage, business rules, data definitions, and engineering standards.
</div></li><li><div>Evaluate the current integration landscape, including manual feeds, direct SQL access, and middleware processes, and help transition the organization to more controlled and scalable patterns.
</div></li></ul><div><div><b>Qualifications and Experience:</b></div></div><p>
</p><ul><li><div>Bachelor&rsquo;s degree in Computer Science, Information Systems, Data Engineering, Analytics, or a related field is preferred. Equivalent practical experience in enterprise data engineering will also be considered.
</div></li><li><div>5+ years of experience in data engineering, data integration, or enterprise data platform development.
</div></li><li><div>4+ years of hands-on experience with Azure data services such as Azure Data Factory, Azure SQL, Azure Data Lake, Microsoft Fabric, Synapse, or closely related Microsoft data technologies.
</div></li><li><div>Strong SQL development skills and experience optimizing complex data transformations.
</div></li><li><div>Experience with Python, PySpark, Spark SQL, or comparable scripting/programming tools used in modern data engineering.
</div></li><li><div>Experience designing and implementing data lake, lakehouse, or data warehouse solutions.
</div></li><li><div>Strong understanding of dimensional modeling, semantic modeling, and data structures used for reporting and analytics.
</div></li><li><div>Experience building and supporting ETL/ELT pipelines across multiple source systems.
</div></li><li><div>Experience with Power BI datasets/semantic models and the data engineering practices that support governed reporting environments.
</div></li><li><div>Experience with data quality, reconciliation, lineage, and documentation practices.
</div></li><li><div>Ability to work in a fast-moving environment where source data, requirements, and processes are still maturing.
</div></li></ul><ul><li><div>Strong communication skills and the ability to work directly with both technical teams and business leaders.
</div></li><li><div>This role is based onsite in Mason</div></li></ul></div>

Data Engineer

CarMax

<div>8117 - Dallas Technology Hub - 5830 Granite Parkway, Plano, Texas, 75024<p></p>
CarMax, the way your career should be!<p><b>
About this job</b></p><p>
The bulk of the data engineer&rsquo;s work would be in building, managing and optimizing data pipelines and then moving these data pipelines effectively into production for key data and analytics consumers (like business/data analysts, data scientists or any persona that needs curated data for operational and analytical data use cases.</p><p></p><p>
Data engineers also need to guarantee compliance with data governance and data security requirements while creating, improving and operationalizing these integrated and reusable data pipelines. This would enable faster data access, integrated data reuse and vastly improved time-to-solution for CarMax&rsquo;s operational and analytical initiatives</p><p><b>
Data Engineer II &ndash; Enterprise Data Services</b></p><p>
We are seeking a Data Engineer with hands‑on, production experience building and operating data pipelines in a cloud environment. This role sits on the <b>Enterprise Data Services</b> team, which owns streaming data ingestion, curation, and delivery for Analytics and Data Science teams.</p><div></div><p><b>
Key Responsibilities</b></p><ul><li>
Design, build, and maintain production-grade data pipelines (streaming and batch)</li><li>
Implement event-driven ingestion and near real‑time processing</li><li>
Build transformations using distributed data processing frameworks</li><li>
Write and maintain clean, testable Python code following software engineering best practices</li><li>
Support CI/CD pipelines, automated deployments, and environment promotions</li><li>
Monitor and troubleshoot pipeline failures, latency issues, and data quality problems</li><li>
Collaborate closely with Data Scientists, Analysts, and platform engineers</li></ul><div></div><p><b>
Required Experience &amp; Skills</b></p><p>
Candidates <b>must</b> have professional, hands‑on experience in most of the following:</p><ul><li>
2+ years of experience as a Data Engineer or Software Engineer working with data</li><li>
Strong experience writing production Python code</li><li>
Experience building and supporting pipelines on Azure or an equivalent cloud platform (AWS/GCP)</li><li>
Hands‑on experience with distributed data processing, such as:<ul><li>
Apache Spark</li><li>
Databricks (or Spark runners such as EMR, Dataproc)</li></ul></li><li>
Experience working with event streaming or messaging platforms, such as:<ul><li>
Azure Event Hubs</li><li>
Apache Kafka / Confluent Kafka</li><li>
Amazon Kinesis</li></ul></li><li>
Practical experience with CI/CD pipelines, version control, and automated deployments</li><li>
Familiarity with data modeling, schema management, and data reliability concepts</li><li>
Experience operating systems in an agile, collaborative engineering team</li></ul><div></div><p><b>
Strongly Preferred</b></p><ul><li>
Supporting production streaming systems with uptime and latency expectations</li><li>
Azure-native services such as:<ul><li>
Azure Databricks</li><li>
Azure Functions (or equivalent serverless frameworks: AWS Lambda, Cloud Functions)</li><li>
Cosmos DB (or comparable NoSQL stores such as DynamoDB, Cassandra)</li></ul></li><li>
Experience working with Analytics or Data Science platform teams</li><li>
Debugging live pipelines and handling operational ownership/on‑call responsibilities</li></ul><div></div><p><b>
Technology Environment (Primary &amp; Equivalent)</b></p><ul><li><b>
Languages:</b> Python (required), SQL, SparkSQL</li><li><b>
Streaming:</b> Event Hubs | Kafka | Kinesis</li><li><b>
Processing:</b> Databricks | Apache Spark | EMR | Dataproc</li><li><b>
Storage:</b> Cosmos DB | DynamoDB | Cassandra</li><li><b>
Serverless:</b> Azure Functions | AWS Lambda</li><li><b>
Cloud:</b> Azure (primary) | AWS | GCP</li><li><b>
DevOps:</b> CI/CD pipelines, automated deployments</li></ul><div></div><p><b>
Who This Role Is For</b></p><p>
This role is a strong fit for someone who:</p><ul><li>
Has already worked on real, production data systems</li><li>
Wants to deepen experience in streaming and cloud‑native data platforms</li><li>
Brings a software engineering mindset to data problems</li><li>
Values ownership, reliability, and continuous learning</li></ul><p></p><p><b>
Work Location and Arrangement: </b>This role will be based out of the CarMax Midtown office, Richmond VA or CarMax Technology Hub, Plano TX and have a Hybrid work arrangement.
</p><ul><li><p>Associates based in Richmond <b>work onsite 4 days per week.</b></p></li><li><p>
Associates based in Plano <b>work onsite 2 days per week.</b></p></li></ul><p><b><br>
</b></p><p><b>Work Authorization:</b> Applicants must be currently authorized to work in the United States on a full-time basis. <b>Sponsorship will not be considered for this specific role.</b></p><p></p><p><b>
About CarMax</b></p><p>
CarMax disrupted the auto industry by delivering the honest, transparent and high-integrity experience customers want and deserve. This innovative thinking around the way cars are bought and sold has helped us become the nation&rsquo;s largest retailer of used cars, with over 250 locations nationwide.</p><p>
Our amazing team of more than 25,000 associates work together to deliver iconic customer experiences. Along the way, we help every associate grow their career and achieve their best, at work and in their community. We are recognized for our commitment to training and diversity and are one of the FORTUNE 100 Best Companies to Work For&reg;.</p><p></p><p>
Our Commitment to Diversity and Inclusion:</p><p>
CarMax is committed to bringing together people from different backgrounds and perspectives, providing employees with a safe, welcoming, and inclusive work environment.</p><p></p><p>
CarMax is an equal opportunity employer, and all qualified candidates will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, protected veteran status, disability status, or any other characteristic protected by law.</p><p></p><p>
Upon an applicant's request, CarMax will consider reasonable accommodation to complete the CarMax Job Application.</p></div>

Engineer II, Data

The Home Depot

<div><p><b>Position Purpose:</b></p><p>
The Staff Data Engineer is a senior technical leader responsible for designing, building, and operating enterprise-scale data platforms and data products within the Enterprise Data Warehouse (EDW). This includes foundational data domains, semantic layers and downstream analytics consumers. This engineer will help define architectural standards, drive platform modernization, and mentor other engineers while remaining hands-on in complex backend data engineering efforts. The role emphasizes scalability, performance, cost efficiency, and analytical correctness across the EDW ecosystem.</p><p></p><p><b><br>
Key Responsibilities:</b></p><ul><li>
50% Delivery and Execution - Develops, tests, deploys, and maintains software, with a clear understanding of the value the software is to provide; Takes a broad view when approaching issues; using a global lens; Consistently achieves results, even under tough circumstances; Develops test suites (functional, destructive, etc) to enable success, rapid deployment of code to production; Takes on new opportunities and tough challenges with a sense of urgency, high energy and enthusiasm; Consistently achieves results, even under tough circumstances</li><li>
10% Learns and Grows - Actively seeks ways to grow and be challenged using both formal and informal development channels; Learns through successful and failed experiment when tackling new problems</li><li>
20% Plans and Aligns - Creates new and better ways for the organization to be successful; Delivers multi-mode communications that convey a clear understanding of the unique needs of different audiences; Works the Product Team to ensure user stories are developer ready, easy to understand and testable; Collaborates with other team members in agile processes; Relates openly and comfortably with diverse groups of people; Adapts approach and demeanor in real time to match the shifting demands of different situations</li><li>
20% Supports and Enables - Fields questions from product and engineering teams; Helps grow junior engineers by providing guidance on modern software development frameworks, and leading technical discussions; Notes gaps on the team and provides suggestions for changes to make the team more productive</li></ul><p><b><br>
Direct Manager/Direct Reports:</b></p><ul><li>
This position typically reports to Software Engineer Manager or Sr. Manager</li><li>
This position typically has 0 Direct Reports</li></ul><p><b><br>
Travel Requirements:</b></p><ul><li>
No travel required.</li></ul><p><b><br>
Physical Requirements:</b></p><ul><li>
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.</li></ul><p><b><br>
Working Conditions:</b></p><ul><li>
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.</li></ul><p><b><br>
Minimum Qualifications:</b></p><ul><li>
Must be eighteen years of age or older.</li><li>
Must be legally permitted to work in the United States.</li></ul><p><b><br>
Preferred Qualifications:</b></p><ul><li>
5+ years of experience in backend data engineering or analytics platform engineering</li><li>
Strong hands-on experience with cloud data warehouses (BigQuery strongly preferred)</li><li>
Advanced SQL expertise and strong working knowledge of Python</li><li>
Proven experience designing enterprise-grade data models, including curated/ADS datasets</li><li>
Experience building and supporting semantic layers (AtScale preferred, but not required)</li><li>
Experience with universal or shared semantic modeling across multiple domains or products</li><li>
Experience with CI/CD and orchestration frameworks, including Jenkins and Airflow</li><li>
Strong understanding of performance tuning and cost optimization techniques in large data environments</li><li>
Experience designing systems for high availability, resilience, and scalability</li><li>
Ability to work across teams and influence architectural decisions without direct authority</li></ul><p><b><br>
Minimum Education:</b></p><ul><li>
The knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job.</li></ul><p><b><br>
Preferred Education:</b></p><ul><li>
No additional education</li></ul><p><b><br>
Minimum Years of Work Experience:</b></p><ul><li>
3</li></ul><p><b><br>
Preferred Years of Work Experience:</b></p><ul><li>
No additional years of experience</li></ul><p><b><br>
Minimum Leadership Experience:</b></p><ul><li>
None</li></ul><p><b><br>
Preferred Leadership Experience:</b></p><ul><li>
None</li></ul><p><b><br>
Certifications:</b></p><ul><li>
None</li></ul><p><b><br>
Competencies:</b></p><ul><li>
Global Perspective</li><li>
Manages Ambiguity</li><li>
Nimble Learning</li><li>
Self-Development</li><li>
Collaborates</li><li>
Cultivates Innovation</li><li>
Situational Adaptability</li><li>
Communicates Effectively</li><li>
Drives Results</li><li>
Interpersonal Savvy</li></ul></div><p></p>

Staff Software Engineer - Enterprise Data Warehouse (Remote)

Flatgigs

<div>
 <strong>
  The Role
  <br/>
  <br/>
 </strong>
 The Forward Deployed AI Engineer will work directly with clients and senior engineers to build AI-powered systems inside real enterprise environments. This role is ideal for a strong junior engineer who wants to work on practical AI systems, enterprise data, agents, retrieval, workflow automation, and context graphs. You will help connect to client systems, process messy real-world data, build prototypes, and turn business problems into working software.
 <br/>
 <br/>
 The work is hands-on and client-facing:
 <br/>
 <br/>
 <ul>
  <li>
   Understand operational workflows and convert them into technical artifacts
  </li>
  <li>
   Connect to real enterprise systems and data sources
  </li>
  <li>
   Build context-aware AI agents, retrieval systems, and automation prototypes
  </li>
  <li>
   Help move the best prototypes toward reliable production deployments
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Requirements
  <br/>
  <br/>
 </strong>
 <strong>
  Build context graphs.
 </strong>
 Help ingest, clean, structure, and connect data from enterprise systems into context graph. This may include structured databases, PDFs, spreadsheets, tickets, CRM data, analytics events, Slack or Teams exports, meeting transcripts, operational workflows, and other internal knowledge sources.
 <br/>
 <br/>
 <strong>
  Develop AI and agentic workflows.
 </strong>
 Build AI pipelines and agent-based systems that can reason over enterprise context, identify patterns, surface workflow gaps, and suggest or trigger automations. This may involve LLMs, retrieval systems, structured extraction, tool use, LangGraph-style workflows, and agent harnesses.
 <br/>
 <br/>
 <strong>
  Connect to enterprise systems
 </strong>
 . Integrate with client infrastructure such as databases, APIs, cloud storage, document repositories, analytics tools, ticketing systems, and internal applications.
 <br/>
 <br/>
 <strong>
  Prototype automation opportunities.
 </strong>
 Rapidly build proof-of-concepts that show how AI can improve a client's operations - for example, process documentation, workflow discovery, incident management, document extraction, customer journey analysis, or operational decision support.
 <br/>
 <br/>
 <strong>
  Turn messy business problems into software.
 </strong>
 Work with senior engineers and business stakeholders to translate ambiguous operational problems into technical designs, data models, prompts, pipelines, and deployed applications.
 <br/>
 <br/>
 <strong>
  Support production deployments.
 </strong>
 Help build reliable, maintainable systems that can run in production environments, including client cloud environments when needed.
 <br/>
 <br/>
 <strong>
  Ideal Candidate
  <br/>
  <br/>
 </strong>
 We are looking for a strong junior software engineer who is excited about applied AI and wants to build real systems, not just experiments.
 <br/>
 <br/>
 You do not need to be an expert in every area below, but you should be curious, technical, and comfortable learning quickly.
 <br/>
 <br/>
 <strong>
  Technical Background
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Strong Python fundamentals
  </li>
  <li>
   Basic backend development experience
  </li>
  <li>
   Familiarity with APIs, databases, and cloud services
  </li>
  <li>
   Interest in LLMs, agents, retrieval, structured outputs, and tool-calling
  </li>
  <li>
   Familiarity with data pipelines and messy real-world datasets
  </li>
  <li>
   Experience with FastAPI, LangChain, LangGraph, vector databases, document processing, or knowledge graphs is a plus
  </li>
  <li>
   AWS experience is a plus
  </li>
  <li>
   Experience with enterprise data, analytics, or workflow automation is a strong plus
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  What Makes Someone Successful In This Role
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   You like figuring out how businesses actually operate
  </li>
  <li>
   You are comfortable working with incomplete, messy, or poorly documented data
  </li>
  <li>
   You can move quickly from vague requirements to a working prototype
  </li>
  <li>
   You care about building useful systems, not just impressive demos
  </li>
  <li>
   You communicate clearly with both engineers and non-technical stakeholders
  </li>
  <li>
   You are curious about how AI agents can interact with real tools, data, and workflows
  </li>
  <li>
   You want to learn how to deploy AI into real enterprise environments
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Why This Role Is Interesting
  <br/>
  <br/>
 </strong>
 You will work on the frontier of practical enterprise AI: connecting AI agents to real company context and using that context to discover and automate high-value workflows.
 <br/>
 <br/>
 This is a hands-on engineering role for someone who wants to grow into building production-grade AI systems across data, agents, infrastructure, and enterprise software
</div>


Forward Deployed AI Engineer

Stellar Solutions

<div><h2 class="jobSectionHeader"><b>Azure Data Engineer</b></h2>
<p><b>Location:</b> Wilmington, MA (Hybrid - 2 days onsite, remainder remote; must be local to MA or NH)</p>
<p><b>Duration:</b> Long Term Contract</p>
<h3 class="jobSectionHeader"><b>Position Summary</b></h3>
<p>We are seeking a highly motivated Azure Data Engineer with 7-9 years of experience to join our enterprise data engineering team. The ideal candidate will bring strong expertise in SQL, Python, and Azure cloud services, with a solid understanding of data warehousing concepts and hands‑on experience in Azure Data Factory and Synapse. Exposure to Microsoft Fabric is preferred but not required.</p>
<p>This role will focus on supporting data pipeline development, migration, and optimization efforts, enabling scalable and reliable data solutions across Finance, Operations, and Enterprise functions.</p>
<h3 class="jobSectionHeader"><b>Key Responsibilities</b></h3>
<ul><li><p>Support <b>data pipeline development and migration</b> from legacy data warehouses to <b>Azure Fabric Lakehouse/Warehouse</b>.</p>
</li><li><p>Write efficient <b>SQL queries</b> (T‑SQL preferred) for extraction, transformation, and validation across multiple databases (SQL Server 2022, Azure SQL MI, Synapse).</p>
</li><li><p>Develop and debug <b>Python scripts</b> for automation, ingestion, and data quality checks.</p>
</li><li><p>Assist in building <b>Fabric dataflows, pipelines, and lakehouse tables</b> aligned with enterprise data‑modeling standards.</p>
</li><li><p>Collaborate with senior engineers on <b>ETL design, optimization, and performance tuning</b>.</p>
</li><li><p>Perform <b>unit testing, peer code reviews</b>, and maintain documentation in Confluence/Git.</p>
</li><li><p>Monitor and troubleshoot <b>data load processes</b>, resolving job failures and updating JIRA/Zephyr test cases.</p>
</li><li><p>Continuously learn and apply emerging Azure technologies (Databricks, Fabric, Power BI) to improve processes.</p>
</li></ul><h3 class="jobSectionHeader"><b>Required Skills &amp; Qualifications</b></h3>
<ul><li><p>Bachelor&rsquo;s degree in Computer Science, Information Systems, Engineering, or related field.</p>
</li><li><p>7-9 years of professional experience in <b>data engineering</b>.</p>
</li><li><p>Strong proficiency in <b>SQL</b> (T‑SQL preferred).</p>
</li><li><p>Hands‑on experience with <b>Python scripting</b>.</p>
</li><li><p>Solid understanding of <b>data warehousing concepts</b> (ETL, staging, fact/dimension modeling).</p>
</li><li><p>Strong expertise with <b>Azure services</b>: Data Factory, Synapse (Fabric experience a plus).</p>
</li><li><p>Experience with <b>Git/Azure DevOps, Confluence, JIRA</b>.</p>
</li><li><p>Knowledge of <b>data modeling, metadata management, and data quality principles</b>.</p>
</li><li><p>Familiarity with <b>Agile/Scrum environments</b>.</p>
</li><li><p>Exposure to <b>Power BI</b> and basic <b>DAX concepts</b>.</p>
</li><li><p>Strong analytical mindset, attention to detail, and enthusiasm for learning enterprise‑scale systems.</p></li></ul></div>

Azure Data Engineer

netPolarity, Inc. (Saicon Consultants, Inc.)

<div>
 <p>
  <strong>
   Title:
   <u>
    Software Engineer
   </u>
  </strong>
 </p>
 <p>
  <strong>
   Location: Mountain View, CA
  </strong>
 </p>
 <p>
  <strong>
   Compensation: $80 - $95 an hour on W2
  </strong>
 </p>
 <p>
  <strong>
   Duration: 5-6 months
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  Description
 </p>
 <p>
  Role Summary
 </p>
 <p>
  Build the core software foundation for simulation-based systems in specialty domains. You&rsquo;ll develop high-performance computation services, workflow orchestration, and enterprise-grade integrations that produce reproducible, traceable outputs suitable for enterprise and main stream users.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Responsibilities
 </p>
 <ul>
  <li>
   Design and implement product features, workflows in C+/Python (e.g., numerical routines, algorithms, user interfaces)
  </li>
  <li>
   Build orchestration for multi-step workflows (project state, job execution, persistence, versioning).
  </li>
  <li>
   Implement robust API contracts and data schemas for results, metadata, and provenance.
  </li>
  <li>
   Integrate with enterprise data sources, cloud based systems, GPUs.
  </li>
  <li>
   Engineer for production quality: Testing, performance profiling, reliability patterns, observability, secure coding, and maintainability.
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  Required Qualifications
 </p>
 <ul>
  <li>
   10+ years production software development; BS/MS in CS/CE/EE or equivalent.
  </li>
  <li>
   Strong C/C++ and Python experience; strong foundation in data structures and algorithms.
  </li>
  <li>
   Experience with AI assisted programming and testing.
  </li>
  <li>
   Experience in UI and UX development.
  </li>
  <li>
   Experience designing, implementing, and operating backend services (REST/gRPC), plus CI/CD and automated testing.
  </li>
  <li>
   Differentiating Capabilities
  </li>
  <li>
   Experience with geometry/CAD ingestion or computational geometry (useful in shape-driven analysis).
  </li>
  <li>
   SaaS/Cloud based development and testing.
  </li>
  <li>
   GPU acceleration, AI Agents, Models
  </li>
  <li>
   Hybrid interactive + batch execution patterns
  </li>
 </ul>
 <p>
 </p>
</div>


Software Engineer

Agile Resources, Inc.

<div>
 <strong>
  Job Title:
 </strong>
 SQL Data Engineer
 <br/>
 <br/>
 <strong>
  Location:
 </strong>
 Denver, CO (Hybrid &ndash; 2+ days onsite required)
 <br/>
 <br/>
 <strong>
  Job Type:
 </strong>
 Contract (12 months, potential for full-time conversion)
 <br/>
 <br/>
 <strong>
  Overview
  <br/>
  <br/>
 </strong>
 We are seeking an experienced SQL Data Engineer to join our team in the Denver area. This role will focus on building and maintaining scalable data pipelines, integrating data across enterprise systems, and supporting high-volume data processing environments. The ideal candidate thrives in a fast-paced setting and can work cross-functionally with both technical and business stakeholders.
 <br/>
 <br/>
 <strong>
  What You&rsquo;ll Do
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Build and maintain ETL/ELT pipelines and data integrations
  </li>
  <li>
   Develop and optimize data models and data warehouse structures
  </li>
  <li>
   Integrate data across systems, including Salesforce, APIs, and enterprise platforms
  </li>
  <li>
   Ensure data quality, accuracy, and performance across pipelines
  </li>
  <li>
   Troubleshoot data issues and pipeline failures
  </li>
  <li>
   Support CI/CD and deployment processes for data pipelines
  </li>
  <li>
   Collaborate with business and technical teams to translate requirements into scalable data solutions
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   4&ndash;7+ years of experience in Data Engineering
  </li>
  <li>
   Strong SQL expertise, specifically with Microsoft SQL Server
  </li>
  <li>
   Hands-on experience with ETL tools, particularly SSIS
  </li>
  <li>
   Solid understanding of data warehousing and data modeling concepts
  </li>
  <li>
   Experience working with large datasets and high-volume systems
  </li>
  <li>
   Familiarity with API-based integrations
  </li>
  <li>
   Strong troubleshooting and problem-solving abilities
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with cloud platforms such as AWS, Azure, Snowflake, or Redshift
  </li>
  <li>
   Experience working with Salesforce data or CRM integrations
  </li>
  <li>
   Exposure to MuleSoft or similar integration platforms
  </li>
  <li>
   Familiarity with CI/CD or DevOps practices for data pipelines
  </li>
  <li>
   Experience in enterprise or large-scale environments
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Soft Skills
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Ability to work independently in a fast-paced environment
  </li>
  <li>
   Strong communication skills with both technical and non-technical stakeholders
  </li>
  <li>
   Comfortable collaborating across multiple teams, including Data, Salesforce, and Integration teams
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Engagement Details
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Duration: 12 months
  </li>
  <li>
   Conversion: Potential to convert to full-time
  </li>
  <li>
   Work Model: Hybrid (Denver, CO &ndash; 2+ days onsite required)
   <br/>
   <br/>
  </li>
 </ul>
 #AB-2
 <br/>
 <br/>
 #ADCC
</div>


SQL Data Engineer

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