• Analyze business workflows and identify opportunities for data automation and AI-driven process automation • Design, build, and maintain scalable data pipelines and integrations, including ingestion from unreliable or unstructured 3rd party sources • Build LLM- and agent-based solutions for data transformation, validation/testing, and extraction tasks • Containerize data and AI workloads using Docker and deploy to cloud infrastructure (AWS) • Develop prototypes and POCs to validate ideas quickly — both data pipelines and AI-powered workflows • Collaborate with business and technical teams to refine requirements and iterate on solutions • Support the deployment and integration of data and AI solutions into production systems • Continuously improve data processes through automation and AI-driven approaches • Contribute to data modeling, quality, and observability practices

Role Description - Analyze business workflows and identify opportunities for data automation and AI-driven process automation. - Design, build, and maintain scalable data pipelines and integrations, including ingestion from unreliable or unstructured 3rd party sources. - Build LLM- and agent-based solutions for data transformation, validation/testing, and extraction tasks. - Containerize data and AI workloads using Docker and deploy to cloud infrastructure (AWS). - Develop prototypes and POCs to validate ideas quickly — both data pipelines and AI-powered workflows. - Collaborate with business and technical teams to refine requirements and iterate on solutions. - Support the deployment and integration of data and AI solutions into production systems. - Continuously improve data processes through automation and AI-driven approaches. - Contribute to data modeling, quality, and observability practices. Qualifications - 3+ years of experience in Data Engineering. - Strong data engineering background with the ability to design and own solutions end-to-end. - Proficiency in Python, Airflow, dbt, and Redshift for data processing, pipeline development, and transformation. - Experience building and maintaining ETL / ELT pipelines and data integrations, including fetching and normalizing data from non-robust 3rd party sources. - Hands-on experience with LLM/agent-based automation applied to business processes (e.g., building agents or LLM-powered workflows for data transformation, testing, or extraction). - Practical familiarity with modern AI tooling — LLM APIs (OpenAI, Anthropic, etc.), RAG patterns, prompt engineering, and agent frameworks (LangChain, LlamaIndex, or similar). - Cross-functional flexibility: comfortable stepping beyond pure DE work into adjacent areas — light DevOps (Docker, CI/CD, cloud deployment), backend integration, and basic frontend when a POC requires it. - Excellent communication skills — able to explain technical decisions to non-technical stakeholders. - Self-directed and proactive: able to spot workflow inefficiencies and drive improvements with minimal supervision. - Product thinking: collaborate with business teams, propose solution approaches, build quick POCs, iterate on feedback, and support production deployment. Requirements - 3+ years of experience in Data Engineering. - Strong data engineering background with the ability to design and own solutions end-to-end. - Proficiency in Python, Airflow, dbt, and Redshift for data processing, pipeline development, and transformation. - Experience building and maintaining ETL / ELT pipelines and data integrations, including fetching and normalizing data from non-robust 3rd party sources. - Hands-on experience with LLM/agent-based automation applied to business processes (e.g., building agents or LLM-powered workflows for data transformation, testing, or extraction). - Practical familiarity with modern AI tooling — LLM APIs (OpenAI, Anthropic, etc.), RAG patterns, prompt engineering, and agent frameworks (LangChain, LlamaIndex, or similar). - Cross-functional flexibility: comfortable stepping beyond pure DE work into adjacent areas — light DevOps (Docker, CI/CD, cloud deployment), backend integration, and basic frontend when a POC requires it. - Excellent communication skills — able to explain technical decisions to non-technical stakeholders. - Self-directed and proactive: able to spot workflow inefficiencies and drive improvements with minimal supervision. - Product thinking: collaborate with business teams, propose solution approaches, build quick POCs, iterate on feedback, and support production deployment. Benefits - Direct cooperation with the already successful, long-term, and growing project. - Flexible work arrangements. - Collaborative and supportive team culture. - Truly competitive salary. - Help and support from our caring HR team.

Role Description We are looking for a talented and analytically minded Data Engineer to join our growing team. In this role, you will be at the heart of our data ecosystem; designing and maintaining robust data pipelines, managing our cloud-based data lake on AWS, and transforming raw data into clean, reliable datasets that power business-critical reporting and analytics. Key Responsibilities - Design, build, and maintain scalable ETL/ELT pipelines using orchestration tools such as Apache Airflow, dbt, or equivalent frameworks. - Extract data from diverse sources (APIs, databases, streaming systems) into our AWS-based data lake. - Transform raw, unstructured data into clean, well-modelled datasets ready for analytics and reporting. - Own and evolve our data lake architecture, including multi-zone S3 storage and AWS Glue cataloguing. - Manage relational and non-relational databases, ensuring optimal schema design, indexing, and query performance. - Leverage AWS services (S3, Redshift, Glue, Lambda, EMR) to build scalable, cost-efficient data solutions. - Process and analyse large-scale datasets using big data technologies such as Apache Spark. - Collaborate with analysts and business stakeholders to translate reporting requirements into reliable data models. - Contribute to the design and evolution of our overall data architecture, data governance, and quality standards. - Document systems, data flows, and architectural decisions to a high standard. Qualifications - Proven experience as a Data Engineer or in a similar data-focused engineering role. - Strong proficiency in Python for data engineering tasks; experience with Rust is a significant advantage. - Hands-on experience building and maintaining ETL/ELT pipelines, ideally using Apache Airflow. - Deep knowledge of database management - both relational and non-relational. - Solid experience with AWS cloud services relevant to data engineering (S3, Glue, Redshift, EMR, Lambda, IAM). - Experience working with big data platforms and distributed computing frameworks (e.g. Apache Spark). - Strong understanding of data lake architecture, including storage layers, partitioning, and data cataloguing. - Excellent analytical thinking and problem-solving ability - you enjoy digging into complex data challenges. - Ability to communicate technical concepts clearly to non-technical stakeholders. Bonus Points - Experience with Rust for performance-critical data processing. Benefits - Opportunity to shape the data architecture of a growing organisation from an early stage. - Opportunity to be a thought leader with a wide span of control in a fast-growing startup with experienced mentors. - A challenging and rewarding environment where you can directly impact the future of the company and the industry.

Data Modeler Location: Sterling Heights United States Job Description: Sterling Heights, MI Contract - 1st Shift, 9/80 Pay Rate: $61.22/Hr Benefits: overtime, medical, dental, vision, 401k Travel: 10% - Hybrid: 3 days in office, 2 remote REQUIREMENTS: Data Modeler - Bachelor's degree in data management, Data Science, Math, Statistics or equivalent - Minimum 6 years of relevant experience - Excellent oral and written communications. - Be directly involved in developing data models and process flows as well as liaising with multiple stakeholders - mainly engineering, to document and/or validate the current state or As-Is business process. - Proven ability to work within teams in which your contributions are critical to success. - Proficiency with tools such as Power BI, Visual Basic, MS Excel, MATLAB/Simulink, Mathcad, MagicDraw, Midas, AWS/AWS Redshift, SSAS, Alation, Dataiku, Oracle, SQL. - Experience using entity-relationship diagrams (ERDs), dimensional modeling, normalization techniques, and metadata repositories to design and document data structures. - Understanding of data warehousing concepts. - Experience with big data technologies like Hadoop or Spark. - Familiarity with cloud-based data services such as AWS or Azure. - Background in data governance and data quality concepts. - Excellent problem-solving and analytical skills. - Strong communication and collaboration skills with both technical and non-technical stakeholders. - Experience in Agile or Scrum environments. JOB DESCRIPTION: Data Modeler Work as part of a team designing, building, and maintaining data infrastructure, ensuring accurate and timely data accessibility for teams and applications, often involving large datasets and complex systems. You will be responsible for creating and maintaining data models that serve as blueprints for a Combat Mission System's data pipeline and associated management strategy. You will translate complex business requirements into structured data representations, ensuring that data flows seamlessly across systems and supports analytics, reporting, and operational needs. Their work is critical for maintaining data integrity, consistency, and accessibility. Key Responsibilities: Data Modeler - Design and implement conceptual, logical, and physical data models to represent organizational data accurately. - Collaborate with business analysts, data architects, developers, and other stakeholders to gather requirements and ensure models meet business and technical needs. - Maintain a central data dictionary, enforce data standards, and ensure compliance with data governance policies. - Optimize performance of data infrastructure by tuning data models for efficiency, reduce redundancy, optimize reusability at the right levels and improve database performance. - Design solutions for integrating data across systems and perform reverse engineering of existing databases when necessary. - Develop and maintain documentation, naming conventions, and coding standards to ensure consistency across data systems helping the business to define "what does good look like?" - Ensure that data is stored securely, efficiently, and in a manner that is accessible for analysis and reporting. - Ensuring the accuracy, consistency, and reliability of data, implementing data validation and quality control processes. - Optimize data pipelines for speed, scalability, and performance, ensuring that data is available when and where it's needed. - Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure that data infrastructure meets their needs. - Troubleshoot issues with data infrastructure, monitor system performance, and perform regular maintenance to ensure smooth operation. - Support configuration control, documentation and change impact analysis on the businesses data architecture model including identification of Authoritative Source Of Truth (ASOT) databases, connectors to replicate data from ASOTS to Midas and/or AWS Cloud Environments through data transformation within Power BI. SKILLS PREFERRED: Data Modeler - Experience with structured problem-solving techniques - Knowledge of technical requirements and systems relevant to Combat Vehicles - Experience working with development teams - Knowledge of metrics related to Agile and use of them to report back to team members at all levels - 1+ years of Scrum/Agile management - In-depth knowledge of Engineering and/or manufacturing environments EXPERIENCE PREFERRED: Data Modeler - 10 or more years of relevant work experience EDUCATION PREFERRED: Data Modeler - Master's degree in mathematics, statistics, business, engineering, physical/applied sciences, or management discipline (business administration, accounting, finance, economics or management information technology)