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Job Description Job Summary Neutrino Advisory is looking for a Senior Analytics Engineer to help build modern, scalable, and trusted data foundations that enable organizations to make better business decisions. In this role, you will design, build, and maintain transformation and semantic layers that convert raw data from enterprise platforms into governed, well-tested, documented, and business-ready datasets and metrics. You will play a key role in enabling both centralized and self-service analytics, ensuring that analysts, business stakeholders, and AI-enabled tools can access and generate trustworthy insights from enterprise data. The ideal candidate brings strong software engineering discipline to analytics development, including version control, automated testing, code reviews, and continuous integration and deployment. You will work closely with business stakeholders, data engineers, BI developers, and technology teams to establish best practices and deliver high-quality data products. This is a hands-on senior-level role with opportunities to influence architecture, establish engineering standards, mentor team members, and contribute to Neutrino's growing Data & Analytics practice. Key Responsibilities • Design, develop, and maintain dbt models on Microsoft Fabric, transforming raw source data into clean, well-documented staging, intermediate, and mart layers using consistent modeling standards and naming conventions. • Design and co-own Power BI semantic models, including certified datasets, shared measures, and row-level security, ensuring consistent metric definitions across reports, dashboards, and self-service analytics. • Establish and implement data quality standards through dbt tests, source freshness checks, data contracts, monitoring, and alerting mechanisms. • Implement software engineering best practices for analytics development, including Git-based workflows, pull request reviews, automated CI/CD for dbt and Fabric deployments, and environment management across development, testing, and production. • Collaborate with business stakeholders, finance, operations, and technology teams to translate business requirements into reliable data models, metrics, and analytical solutions. • Establish clear documentation for data models, metrics, business definitions, and data lineage to enable analysts, business users, and AI-powered tools to discover and confidently use enterprise data. • Develop and maintain a catalog of certified datasets, business metrics, and data ownership. • Optimize data transformation pipelines and semantic models for performance, scalability, and cost through incremental loading, partitioning, and efficient query design. • Apply appropriate data access controls, privacy protections, security standards, and auditability requirements in collaboration with information security and compliance teams. • Review technical deliverables, mentor junior analysts and engineers, and promote best practices in analytics engineering and data modeling. • Contribute to the development of reusable templates, technical standards, frameworks, and onboarding documentation. • Evaluate emerging tools, technologies, and architectural approaches to continuously improve the data and analytics ecosystem. • Troubleshoot complex data and pipeline issues, conduct root cause analysis, and communicate resolutions and business impact effectively to technical and non-technical stakeholders. Required Knowledge, Skills & Qualifications • Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, Information Systems, or a related field, or equivalent practical experience. • 6+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence development, with demonstrated experience delivering production-grade data models. • Advanced SQL skills, including query optimization, dimensional modeling, fact and dimension tables, and slowly changing dimensions (SCD). • Hands-on experience with dbt in production environments, including modular model development, testing, documentation, macros, incremental models, and deployment practices. • Experience with Microsoft Fabric, including Lakehouse, Warehouse, OneLake, Data Factory, and data pipelines, or comparable cloud data platforms such as Snowflake, Databricks, or BigQuery. • Strong Power BI experience, including semantic model design, DAX, row-level security, and governance of shared datasets. • Familiarity with AI-enabled analytics tools and the data architecture, governed metrics, metadata, and documentation required to support reliable AI-driven insights. • Working knowledge of Git, pull request workflows, and CI/CD pipelines using tools such as Azure DevOps or GitHub Actions. • Proficiency in Python for scripting, automation, and data validation. • Experience implementing data quality, testing, monitoring, and observability practices. • Ability to translate complex or ambiguous business requirements into clear technical designs and communicate architectural decisions and trade-offs effectively. • Strong written and verbal communication skills, with the ability to collaborate across technical and business teams. • Understanding of data privacy, security, governance, and compliance principles. Preferred Qualifications • Experience in financial services, wealth management, banking, healthcare, or other regulated industries. • Familiarity with CRM, portfolio accounting, financial reporting, or custodial data. • Experience building enterprise-scale analytics platforms using Microsoft Azure and Microsoft Fabric. • Exposure to modern data architecture patterns, including medallion architecture, data lakes, lakehouses, and enterprise data warehouses. • Experience working with AI-powered analytics, natural language querying, and enterprise data governance for AI adoption. What We Offer • Opportunity to work on modern data engineering and analytics transformation initiatives. • Exposure to enterprise-scale implementations across Microsoft Fabric, Power BI, Azure, and modern data platforms. • Opportunity to work with emerging AI technologies and help shape the future of enterprise analytics. • A collaborative, technology-driven environment with opportunities for continuous learning and professional growth. • The opportunity to take ownership, influence technical direction, and contribute to building Neutrino's Data & Analytics practice. About Neutrino Advisory Neutrino Advisory is a Digital Strategy, Advisory & Marketing Technology firm helping organizations navigate digital transformation through data, cloud, CRM, enterprise platforms, and applied AI. We combine business strategy with technology expertise to build scalable solutions that deliver measurable business outcomes. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform crucial job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Summary The Senior Data Integration Engineer is responsible for the design, development, and operation of the data integration and ingestion processes that deliver partner and internal data into our analytics environment. The role owns the flow of data from source acquisition through the curated data warehouse tables consumed by reporting platforms, operational systems, and business analysts. This is a senior, hands-on engineering position spanning the full integration lifecycle: acquiring data from a wide range of external partner and internal systems, validating and conditioning that data on arrival, transforming it into the structures that support analysis and operations, and operating those processes reliably in production. The role is concerned equally with building new integrations and with the continued performance, accuracy, and timeliness of those already in service. A central objective of the position is to advance reusable, well-instrumented integration patterns that shorten the time required to onboard new data sources and that improve the reliability and transparency of data delivery to the business. The Senior Data Integration Engineer will also contribute substantially to the planned modernization of our data platform, evaluating and recommending tooling, architecture, and migration approach for leadership consideration. The role sets technical direction and development standards for data integration work and collaborates closely with data architects, business analysts, stakeholders across the organization, and the technical contacts of our external data partners. Essential Duties and Responsibilities This list of duties and responsibilities is not all inclusive and may be expanded to include other duties and responsibilities as management may deem necessary from time to time. Design, develop, and maintain data integration processes that acquire data from partner and internal sources, including flat file transfers over SFTP, REST API endpoints, and direct database connections. Develop reusable, configuration-driven ingestion patterns that reduce the effort and elapsed time required to onboard new partner data feeds. Develop and maintain the T-SQL transformation logic that carries data from landing and staging layers through to the curated warehouse tables supporting reporting, operational systems, and analyst queries. Design ingestion processes to be idempotent and safely re-runnable, incorporating automated retry and restart behavior for failed executions. Implement automated validation and quarantine processes so that records failing business-defined quality rules are isolated, reported, and prevented from reaching downstream consumers. Implement data quality rules defined by the business, including schema validation, reconciliation, row count and threshold checks, and anomaly detection. Establish monitoring, logging, and alerting for pipeline execution state, data freshness, and load completion, and automate the communication of ingestion status to stakeholders. Diagnose and resolve production data incidents, determine root cause, coordinate remediation, and document preventive measures through runbooks and post-incident review. Contribute to dimensional data model design in collaboration with the data architect and senior team members. Establish and maintain version control, code review, and repeatable deployment practices for database and pipeline code. Define and uphold development standards for data integration work through code review and technical guidance. Evaluate and recommend tooling, architecture, and sequencing for the platform modernization effort for leadership consideration. Migrate established integration workflows to modernized patterns incrementally and without disruption to production operations. Maintain documentation of data feeds, dependencies, lineage, ownership, and escalation paths. Perform all work involving protected health information in accordance with HIPAA requirements, including least-privilege access, secure transmission and storage of partner data, and the exclusion of PHI from logs and non-production environments. Coordinate with partner technical contacts, as needed, to resolve file format, schema, and connectivity questions. Provide occasional off-hours support for critical data load failures or production support rotations as needed to support timely response to critical data issues. Support AI and machine learning initiatives by maintaining reliable, secure, and well-governed data pipelines and datasets used for model development, testing, deployment, monitoring, and ongoing performance evaluation. Maintain confidentiality of information processed & follow company policies and procedures. Qualifications Requires six (6) or more years of professional data engineering, data operations, data platform operations, or related experience. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent professional experience preferred. Experience leading teams and developing supervisory staff preferred. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Other qualifications include: Advanced T-SQL development skills, including: Set-based rewriting of row-by-row and cursor-based logic. MERGE, upsert, and slowly changing dimension load patterns. Window functions and complex analytic queries. Execution plan analysis, index strategy, statistics management, and resolution of performance issues such as parameter sniffing. Transaction management and structured error handling within stored procedures, including correct rollback behavior on partial failure. Demonstrated proficiency in Python for data engineering applications, including API-based data acquisition, file parsing and format handling, data validation, and the development of packaged, scheduled jobs. Familiarity with common data libraries such as requests and pandas is expected. Demonstrated experience acquiring and integrating data from heterogeneous sources, including delimited, fixed-width, JSON, and XML file formats; REST APIs requiring authentication, pagination, and rate-limit handling; and direct database connectivity. Proficiency with Git and collaborative development workflows, including branching, pull requests, and code review. A code-first development approach, with integration logic authored and maintained in T-SQL and Python under source control. Ability to analyze pipeline and query performance and to improve the reliability, scalability, and cost efficiency of data workloads. Strong written communication skills, with the ability to produce runbooks, technical documentation, and incident reports, and to convey the business impact of technical issues to non-technical stakeholders. Strong problem-solving skills, attention to detail, and demonstrated ownership of production systems. Experience with Microsoft Azure data services such as Azure Data Factory or Microsoft Fabric, or comparable cloud orchestration platforms. Experience migrating on-premises SQL Server integration workloads to a cloud platform. Experience with dimensional modeling and data warehouse design. Experience designing and rationalizing SQL Server Agent job dependencies and scheduling. Experience establishing version control, code review, and repeatable deployment practices for database code. Experience implementing CI/CD pipelines for database projects. Experience handling protected health information under HIPAA, or comparably regulated data under an equivalent framework preferred. Experience with healthcare or pharmacy data, including claims, eligibility, prescription, or delivery data preferred. Familiarity with data governance, metadata management, and data lineage practices. Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. (The phrases "occasionally," "regularly," and "frequently" correspond to the following definitions: "Occasionally" means up to 1/3 of working time, "regularly" means between 1/3 and 2/3 of working time, and "frequently" means 2/3 and more working time.) While performing the duties of this job, the employee is frequently required to sit; talk or hear; and use hands to handle, or touch objects or controls. The employee is regularly required to stand and walk. On occasion the incumbent may be required to stoop, bend or reach above the shoulders. The employee would rarely need to lift up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. Work Environment The position is a hybrid position with 2 - 3 days office presence in Milwaukee, WI required. The colleague may perform work-related travel on rare occasions (less than 10%) with an emphasis on travel for impact. . click apply for full job details

Summary The Senior Data Integration Engineer is responsible for the design, development, and operation of the data integration and ingestion processes that deliver partner and internal data into our analytics environment. The role owns the flow of data from source acquisition through the curated data warehouse tables consumed by reporting platforms, operational systems, and business analysts. This is a senior, hands-on engineering position spanning the full integration lifecycle: acquiring data from a wide range of external partner and internal systems, validating and conditioning that data on arrival, transforming it into the structures that support analysis and operations, and operating those processes reliably in production. The role is concerned equally with building new integrations and with the continued performance, accuracy, and timeliness of those already in service. A central objective of the position is to advance reusable, well-instrumented integration patterns that shorten the time required to onboard new data sources and that improve the reliability and transparency of data delivery to the business. The Senior Data Integration Engineer will also contribute substantially to the planned modernization of our data platform, evaluating and recommending tooling, architecture, and migration approach for leadership consideration. The role sets technical direction and development standards for data integration work and collaborates closely with data architects, business analysts, stakeholders across the organization, and the technical contacts of our external data partners. Essential Duties and Responsibilities This list of duties and responsibilities is not all inclusive and may be expanded to include other duties and responsibilities as management may deem necessary from time to time. Design, develop, and maintain data integration processes that acquire data from partner and internal sources, including flat file transfers over SFTP, REST API endpoints, and direct database connections. Develop reusable, configuration-driven ingestion patterns that reduce the effort and elapsed time required to onboard new partner data feeds. Develop and maintain the T-SQL transformation logic that carries data from landing and staging layers through to the curated warehouse tables supporting reporting, operational systems, and analyst queries. Design ingestion processes to be idempotent and safely re-runnable, incorporating automated retry and restart behavior for failed executions. Implement automated validation and quarantine processes so that records failing business-defined quality rules are isolated, reported, and prevented from reaching downstream consumers. Implement data quality rules defined by the business, including schema validation, reconciliation, row count and threshold checks, and anomaly detection. Establish monitoring, logging, and alerting for pipeline execution state, data freshness, and load completion, and automate the communication of ingestion status to stakeholders. Diagnose and resolve production data incidents, determine root cause, coordinate remediation, and document preventive measures through runbooks and post-incident review. Contribute to dimensional data model design in collaboration with the data architect and senior team members. Establish and maintain version control, code review, and repeatable deployment practices for database and pipeline code. Define and uphold development standards for data integration work through code review and technical guidance. Evaluate and recommend tooling, architecture, and sequencing for the platform modernization effort for leadership consideration. Migrate established integration workflows to modernized patterns incrementally and without disruption to production operations. Maintain documentation of data feeds, dependencies, lineage, ownership, and escalation paths. Perform all work involving protected health information in accordance with HIPAA requirements, including least-privilege access, secure transmission and storage of partner data, and the exclusion of PHI from logs and non-production environments. Coordinate with partner technical contacts, as needed, to resolve file format, schema, and connectivity questions. Provide occasional off-hours support for critical data load failures or production support rotations as needed to support timely response to critical data issues. Support AI and machine learning initiatives by maintaining reliable, secure, and well-governed data pipelines and datasets used for model development, testing, deployment, monitoring, and ongoing performance evaluation. Maintain confidentiality of information processed & follow company policies and procedures. Qualifications Requires six (6) or more years of professional data engineering, data operations, data platform operations, or related experience. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent professional experience preferred. Experience leading teams and developing supervisory staff preferred. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Other qualifications include: Advanced T-SQL development skills, including: Set-based rewriting of row-by-row and cursor-based logic. MERGE, upsert, and slowly changing dimension load patterns. Window functions and complex analytic queries. Execution plan analysis, index strategy, statistics management, and resolution of performance issues such as parameter sniffing. Transaction management and structured error handling within stored procedures, including correct rollback behavior on partial failure. Demonstrated proficiency in Python for data engineering applications, including API-based data acquisition, file parsing and format handling, data validation, and the development of packaged, scheduled jobs. Familiarity with common data libraries such as requests and pandas is expected. Demonstrated experience acquiring and integrating data from heterogeneous sources, including delimited, fixed-width, JSON, and XML file formats; REST APIs requiring authentication, pagination, and rate-limit handling; and direct database connectivity. Proficiency with Git and collaborative development workflows, including branching, pull requests, and code review. A code-first development approach, with integration logic authored and maintained in T-SQL and Python under source control. Ability to analyze pipeline and query performance and to improve the reliability, scalability, and cost efficiency of data workloads. Strong written communication skills, with the ability to produce runbooks, technical documentation, and incident reports, and to convey the business impact of technical issues to non-technical stakeholders. Strong problem-solving skills, attention to detail, and demonstrated ownership of production systems. Experience with Microsoft Azure data services such as Azure Data Factory or Microsoft Fabric, or comparable cloud orchestration platforms. Experience migrating on-premises SQL Server integration workloads to a cloud platform. Experience with dimensional modeling and data warehouse design. Experience designing and rationalizing SQL Server Agent job dependencies and scheduling. Experience establishing version control, code review, and repeatable deployment practices for database code. Experience implementing CI/CD pipelines for database projects. Experience handling protected health information under HIPAA, or comparably regulated data under an equivalent framework preferred. Experience with healthcare or pharmacy data, including claims, eligibility, prescription, or delivery data preferred. Familiarity with data governance, metadata management, and data lineage practices. Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. (The phrases "occasionally," "regularly," and "frequently" correspond to the following definitions: "Occasionally" means up to 1/3 of working time, "regularly" means between 1/3 and 2/3 of working time, and "frequently" means 2/3 and more working time.) While performing the duties of this job, the employee is frequently required to sit; talk or hear; and use hands to handle, or touch objects or controls. The employee is regularly required to stand and walk. On occasion the incumbent may be required to stoop, bend or reach above the shoulders. The employee would rarely need to lift up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. Work Environment The position is a hybrid position with 2 - 3 days office presence in Milwaukee, WI required. The colleague may perform work-related travel on rare occasions (less than 10%) with an emphasis on travel for impact. . click apply for full job details