• Responsible for the design and implementation of complex data migrations into ERP systems • Analyze source and target systems to ensure consistent, structured data transfers • Develop, optimize, and standardize tool-based migration processes • Assess data quality, perform validations, and document migration results • Plan and facilitate tests, reviews, and acceptance procedures with clients and internal teams • Technical responsibility in migration and system replacement projects

• Lead the design and implementation of robust pipelines for large-scale data ingestion and processing, both batch and streaming, ensuring low latency, high availability, and resilience. • Define and maintain data architecture standards, including partitioning, versioning, security, and governance, ensuring consistency across domains and consumer teams. • Ensure data reliability, quality, and traceability through observability, monitoring, automated testing, and Data Quality practices across the entire data lifecycle. • Act as the technical point of contact for product and business stakeholders, translating strategic needs into scalable solutions and clearly communicating trade-offs (time, cost, performance, and risk). • Evaluate and recommend technologies and frameworks for the data stack, ensuring continuous evolution with a focus on operational efficiency and maintainability. • Conduct performance and cost diagnostics and optimizations for queries, jobs, storage, and orchestration. • Support the technical development of the team through code reviews, mentoring, and knowledge sharing, raising standards and accelerating engineering best practices. • Collaborate with other disciplines (Analytics Engineers, Data Analysts, Software Engineers, etc.) to integrate end-to-end solutions and unblock dependencies between teams.

• Ingesting lead data from APIs, forms, CSVs, and third-party partners into Salesforce • Standardizing, validating, and mapping incoming data to the correct fields • Managing deduplication using rules, fuzzy matching, or tools like DemandTools • Supporting lead source and UTM attribution for accurate marketing reporting • Improving object relationships across Leads, Contacts, Accounts, and Opportunities • Diagnosing and fixing data quality issues across Salesforce objects • Troubleshooting sync and integration problems (e.g., Zapier, Make.com webhooks) • Analyzing and optimizing our Salesforce architecture for long-term scalability • Recommending new tools, automations, or workflows to improve data operations

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.