Your role at DynatraceDynatrace’s Data & Analytics organization is focused on building a scalable, AI-ready, self-service data ecosystem across the company. As a Senior Analytics Engineer, you will play a key role in designing and delivering high-quality data products that support reporting, self-service analytics, and emerging AI use cases. This is a hands-on technical role for someone who excels at translating business needs into well-structured, reliable analytical data assets. You will work closely with business stakeholders, data engineers, platform engineers, and BI teams to develop trusted, scalable, and well-governed datasets. In addition, you bring awareness of modern AI-assisted development workflows, including tools such as GitHub Copilot or Claude, and are comfortable leveraging these to improve productivity, documentation, and data model design. You will contribute to advancing our data modeling practices and help ensure that our data products are consistent, reusable, and aligned with business goals. Key Responsibilities: Data Product Design & Development - Design and develop scalable analytical data models, curated datasets, conformed dimensions, and standardized metrics - Translate complex business requirements into clear, reliable, and reusable data products that support both executive reporting and day-to-day analytics use cases - Contribute to designing data models and metadata structures with AI readiness in mind, including column-level descriptions, consistent naming conventions, and well-documented semantic context - Build and maintain data structures that support discoverability and usability across analytics, BI, and emerging AI/ML use cases Standards, Quality & Governance - Follow and contribute to established standards and best practices for SQL development, dbt modeling, testing, documentation, and data quality - Support the implementation of the semantic layer by ensuring metric definitions and business logic are consistently applied - Implement and maintain data quality checks, governance standards, and security measures within dbt models and Snowflake - Help ensure data models are well-tested, documented, and aligned with business definitions Collaboration & Technical Contribution - Partner with data engineers, platform teams, governance, and business stakeholders to gather requirements and deliver data solutions - Collaborate with team members through code reviews, knowledge sharing, and adoption of best practices - Contribute to cross-functional initiatives focused on improving standardization, consistency, and reuse of analytics assets - Identify opportunities to improve processes, enhance automation, and streamline development workflows What will help you succeedRequired - Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field - 5–8+ years of experience in analytics engineering, data modeling, or related roles with strong focus on SQL programming - Strong SQL skills, including experience with complex analytical queries and performance optimization - Solid hands-on experience with dbt Cloud, including models, tests, and documentation practices - Experience working with Snowflake, including query tuning and understanding of warehouse performance - Strong understanding of dimensional modeling (Kimball) concepts including star schemas, conformed dimensions, and slowly changing dimensions - Experience working within established analytics engineering or data modeling frameworks and standards - Ability to work independently on complex tasks while collaborating effectively with cross-functional teams - Strong communication skills, with the ability to translate technical concepts for business stakeholders - Experience with data quality validation, testing approaches, and governance alignment - Proficiency with Git/GitHub workflows including pull requests and code reviews - Proven experience with AI-assisted development tools such as GitHub Copilot, Claude, or similar, and the ability to leverage them to improve productivity, development workflows, and documentation. - Experience or familiarity with Power BI, including understanding of downstream reporting needs and the ability to support or contribute to BI development when needed Preferred - Master’s degree in a related field - Python experience for automation, scripting, or data validation - Exposure to semantic layer concepts or metric frameworks (e.g., dbt Semantic Layer, Cube, AtScale) - Familiarity with AI/ML data readiness, feature engineering concepts, or designing data for downstream advanced analytics use cases - Experience with data ingestion tools such as Fivetran - Exposure to enterprise systems such as Salesforce, NetSuite, or SuccessFactors Why you will love being a Dynatracer - Dynatrace is a leader in unified observability and security. - We provide a culture of excellence with competitive compensation packages designed to recognize and reward performance. - Our employees work with the largest cloud providers, including AWS, Microsoft, and Google Cloud, and other leading partners worldwide to create strategic alliances. - You'll get to work at the forefront of innovation with Dynatrace Intelligence—the industry's first agentic operations system. Bringing together deterministic and agentic AI, it helps teams understand what's happening, why it matters, and what to do next— automatically. - Over 50% of the Fortune 100 companies are current customers of Dynatrace. Compensation and RewardsDue to legal reasons we are obliged to disclose the minimum salary for this position, which is €61,000 gross per year based on full-time employment (38.5 h / week). We offer a higher salary in line with qualifications and experience.

Role Description 829 is hiring a Senior Analytics Engineer to join our Analytics & AI practice and deliver high-impact BI and data engineering work for our clients. This is a dedicated practice role focused entirely on client engagements, working directly with clients to scope projects, design data architecture, and build solutions that turn raw data into business outcomes. This is a hands-on, individual contributor role with significant client-facing responsibility. You will: - Run discovery, define requirements, design data models, build ETL pipelines, develop dashboards, and present insights to client stakeholders ranging from analysts to C-suite executives. - Own the end-to-end delivery of analytics engagements and serve as the trusted technical advisor to the clients you work with. - Be comfortable writing SQL and dbt models, leading workshops with a client's marketing leadership team, and working through ambiguous data problems independently. This role sits within 829's Analytics & AI practice and reports to the SVP of Analytics & AI. Qualifications - Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field. - 5+ years of experience in BI solution development and implementation, ideally in a consulting or agency environment. - Demonstrated experience leading client-facing engagements and presenting to executive stakeholders. - 3+ years of hands-on Python experience, including data manipulation, automation, and pipeline development. - 4+ years of strong SQL experience, preferably with Snowflake or BigQuery. - 4+ years of experience building dashboards and reports in modern BI tools (Tableau, Sigma, or similar). - Working experience with dbt for data transformation and modeling. - Proven experience designing ETL processes, data models, and BI solution architectures from the ground up. - Knowledge of AI/ML operations and experience maintaining models in production. - Strong problem-solving, communication, and project management skills. - Ability to work independently and deliver results in ambiguous situations. Requirements - Set the technical direction for client engagements and contribute to practice-wide delivery standards. - Design and implement data architectures for clients using Snowflake, BigQuery, dbt, and modern data stack tools. - Build and maintain ETL/ELT pipelines using Python, SQL, and orchestration platforms. - Drive data architecture recommendations and establish best practices across engagements. - Develop and refine reusable templates, frameworks, and assets that improve delivery speed and quality across the practice. - Stay current on tooling, techniques, and AI/ML approaches relevant to client work, and bring those forward into engagements where they create value. Benefits - Remote Workplace: Option to work at our office in Boston or remotely in various states. - Paid Time Off: Generous paid vacation benefits that increase each year, 12 Company Holidays, and Summer Fridays from Memorial Day through Labor Day. - 401K + Match: 401K plan with 4% Safe Harbor employer match after one year of employment. - Life Insurance Benefit: No-cost coverage to ensure peace of mind for your family. - Short Term Disability Benefit: Coverage for illness or injury that keeps you out of work. - Healthcare: Competitive healthcare plans with significant employer coverage. - Commuter Benefits: Pre-tax funds allocation towards your commute. - Continuing Education: Access to webinars, learning platforms, and funding for conferences.

• Model raw data into clean, tested, and reusable datasets. • Since data models are created around business needs, the job of analytics engineers is to define the rules and requirements for the formats and attributes of data. • Translate user and product requirements into data model requirements. • Builds ETL pipelines that can efficiently process very large datasets. • Design, implement and maintain online and offline feature stores to support ML training and inference. • Develop and maintain data and design documentation ensuring all team members are executing against the same architectural vision. • Draft and maintain documents that describe how the data flows from data sources to consumption by visualizing them with directed acyclic graphs (DAGs). • Define metrics and implement tests to guarantee data meets operational and analytics needs. • Responsible for implementing data quality standards across the organization. • Develop and maintain automation, scheduling and monitoring of processes designed to gather data from disparate sources and preparing them for data analysis. • Use CI/CD processes throughout the data model development lifecycle to develop higher quality code and data models without disruption to production.

• Work on building and evolving the organization’s analytical layer, transforming raw data into reliable, documented, and reusable data products. • Be responsible for structuring analytical models, Data Marts, and corporate metrics that support Business Intelligence, Analytics, and Machine Learning initiatives. • Ensure data quality, governance, scalability, and alignment with business needs. • Develop transformations and analytical models using DBT. • Build, maintain, and evolve analytical data layers, including Silver, Curated, and Gold. • Design and implement Data Marts oriented to business area needs. • Translate business rules and processes into scalable, standardized, and reusable data models. • Define, document, and govern corporate metrics, promoting consistency across teams. • Ensure data quality, traceability, observability, and reliability. • Develop automated tests and quality controls to validate data. • Support the provisioning of datasets for consumption by BI, Analytics, and Machine Learning teams. • Collaborate with Data Engineers, Data Scientists, BI Analysts, and business stakeholders. • Promote best practices in analytical modeling, documentation, versioning, and data governance. • Contribute to the evolution of analytical architecture and the organization of data layers. • Participate in defining standards and strategies for consuming corporate data. • Identify opportunities for optimization, performance improvements, and scalability of developed solutions.