About NuView Analytics At NuView Analytics, we help companies accelerate the time to insights from their data. We do this in three ways: data analytics, data diligence, and fractional data science. Our clients are growth-stage companies looking to drive additional value from the data they are sitting on. Through our values of humility, intellectual rigor, and stewardship, we help companies gain a new perspective on their business through their data. The Role We're looking for a Data Engineer to join our growing team and help clients build scalable, reliable data infrastructure. You'll work across the modern data stack, designing pipelines, architecting warehouses, and enabling the analytical layer that our clients depend on. This is a high-impact, client-facing role that combines deep technical execution with strategic thinking. Responsibilities - Design, build, and maintain scalable data pipelines for clients across industries - Architect and optimize cloud data warehouse solutions, adapting to each client's stack, which may include Snowflake, BigQuery, Redshift, Microsoft Fabric, or similar platforms - Lead data integration projects from source system to analytical layer, including scoping, delivery, and handoff - Work fluidly across a range of modern data tools and platforms as client engagements demand, picking up new technologies quickly and applying best practices regardless of the toolset - Collaborate with analysts and data scientists to ensure data is clean, reliable, and well-modeled - Champion data quality, testing, and observability best practices across client engagements - Produce and maintain clear technical documentation including pipeline architecture, data dictionaries, lineage maps, and runbooks so clients can understand and own their infrastructure long-term - Document engineering decisions, standards, and workflows in a way that supports knowledge transfer to both clients and junior team members - Research and evaluate new technologies and advocate for tooling investments that benefit the firm - Train and mentor junior team members on engineering standards, pipeline design, and best practices - Participate in client-facing communication, including requirements gathering and progress updates - Flex support when capacity allows: contribute to analyst-side deliverables such as Power BI dashboard development, ad-hoc reporting, or data visualization. We're a lean team and value versatility Projects Include - ETL/ELT pipeline development and optimization - Data warehouse modeling (dimensional, medallion/lakehouse architectures) - Data integration across client systems such as CRM, ERP, marketing, and operational systems - Infrastructure setup across the modern data stack (ingestion, transformation, orchestration) - Implementations across platforms such as Microsoft Fabric, Databricks, and Snowflake, meeting clients where they are - Data modeling and deployment across medallion architecture layers (bronze, silver, gold) - Data quality frameworks and automated pipeline testing - Cloud infrastructure provisioning and cost optimization (Azure, AWS, GCP) - Technical documentation projects including data dictionaries, ER diagrams, lineage documentation, and metrics catalogs - Power BI semantic model development and dashboard support when business needs require it Qualifications - Bachelor's Degree in Computer Science, Engineering, Mathematics, or a related field - 2–5+ years of relevant data engineering or software engineering experience - SQL Expert: complex query authoring, query optimization, stored procedures - Python Required: pipeline scripting, automation, data processing - Transformation Tools: dbt required; Spark experience a plus - Ingestion Tools: Fivetran, Airbyte, Rivery, Microsoft Fabric Data Factory, or similar - Orchestration: Airflow, Prefect, Azure Data Factory, Microsoft Fabric, or equivalent - Cloud Platforms: Azure (preferred), AWS, or GCP experience - Data Warehouses: Snowflake, BigQuery, Redshift, Microsoft Fabric, Azure Synapse, or equivalent - Version Control: Git required; branching strategies, pull requests, and code review workflows - Strong communication skills with the ability to translate technical concepts for non-technical stakeholders - Self-starter who thrives in a remote environment and can manage multiple client workstreams - Player-coach mindset: capable of leading projects while growing junior teammates - Intellectually curious about evolving data tooling, architecture patterns, and AI-augmented engineering NuView Analytics is an equal opportunity employer. We celebrate diverse perspectives and are committed to building an inclusive team.

Role Description GDIT is looking to hire a Sr. Data Engineer Principal. The qualified individual architects and implements high-throughput, low-latency data pipelines. This position provides critical technical expertise in support of Special Operations Forces (SOF). You will help design and implement cutting-edge, data-driven systems that collect, interpret, and report performance information on high-performance vehicles and their sub-systems. Your work will enable advanced insight into logistics, operational performance, predictive maintenance, and lifecycle extension for these mission-essential platforms. This is an opportunity to directly support the Nation’s most elite forces—bringing your technical talent to bear on real-world challenges that matter. Qualifications - MS with 10+ years data engineering experience. - Strong SQL tuning and distributed compute expertise. - Security clearance level: Must be able to obtain Secret clearance. - Preferred: Experience with time-series databases or tactical edge systems. Requirements - Suggested Technical Stack: - Python, Scala, SQL. - Streaming: Kafka, Flink, Spark. - Orchestration: Airflow, Dagster, Prefect. - Storage: Delta Lake, Iceberg, Snowflake. - Quality: Great Expectations, OpenMetadata. Benefits - Growth: AI-powered career tool that identifies career steps and learning opportunities. - Support: An internal mobility team focused on helping you achieve your career goals. - Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off. - Flexibility: Full-flex work week to own your priorities at work and at home. - Community: Award-winning culture of innovation and a military-friendly workplace. Company Description We are GDIT. A global technology and professional services company that delivers consulting, technology and mission services to every major agency across the U.S. government, defense and intelligence community. Our 30,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50 countries worldwide, offering leading capabilities in digital modernization, AI/ML, Cloud, Cyber and application development. Together with our clients, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.

• Create innovative solutions for handling peta-bytes of data with billions of rows & joins • Program and maintain our data pipelines that fuels our on-premise/cloud data warehouse used to generate and serve our models • Maintain and improve our fleet of data servers (the software), making sure they are reliable and able to process our billions of logs and data points • Develop and productionize data pipelines for our ML models in both bare-metal and the cloud environment • Make suggestions and lead projects to improve our data processing capabilities • Contribute to the team enabling us to be always better

• Partner with the engineering team to lead the knowledge transfer of "Atlas" (built in Python), taking full architectural ownership and ensuring its continued health and progression. • Using Apache Airflow to manage and optimize the daily ingestion of analytics from multiple disparate sources (social media, podcast platforms, etc.) to ensure a clean, reliable, and "healthy" data stream. • Act as a "data detective" to identify what information we are missing and prioritize new data collection that aligns with our financial stability and growth levers. • Build and maintain intuitive dashboards (e.g., Power BI or custom builds) that allow non-technical peers to answer basic everyday questions without needing manual engineering support. • Transform raw data into structured, "customer-ready" packages to support our heavy push into data licensing. • Utilize your knowledge of the media analytics landscape to guide the organization on whether to build custom internal tools or leverage existing 3rd-party social media analytics solutions.