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# Data Analytics Engineer

- **Company:** Microsoft
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $106,400.0 - $203,600.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Microsoft Azure, Batch Processing, Cascading Style Sheets (CSS), Configuration Management, Code Review, Computer Programming, Continuous Delivery, Continuous Integration, Data Architecture, Data Governance, Data Infrastructure, Data Recovery, Dimensional Modeling, Distributed Computing Environment, Python (Programming Language), Machine Learning, Microsoft Software, Language Modeling, Operational Databases, Query Optimization, Raw Data, Power BI, Standard Sql, Software Engineering, SQL Databases, Data Streaming, Systems Integration, Test Data, Management of Software Versions, Usage Analysis, Scripting, Azure Data Factory, Delivery Pipeline, Apache Spark, Microsoft Fabric, Pyspark, Information Technology, Data Analytics, Star Schema, Data Management, Tools for Reporting, Virtual Agents, Azure Synapse Analytics, Multiplatform, Software Version Control, Data Pipelines, Databricks
- **Published:** September 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-data-analytics-engineer-new-york-ny--73a76d30-fad1-400b-b95f-2f81ad474480

## About the Role

* Deep SQL expertise and strong programming skills in Python, including experience with distributed processing frameworks such as Spark or PySpark.
 * Experience building production data pipelines in both batch and streaming or near real-time modes, using platforms such as Microsoft Fabric, Azure Data Factory, Synapse, Databricks, or equivalent.
 * Strong dimensional and semantic modeling skills, including star schema design, tabular or semantic models, calculation languages such as DAX, and the discipline of certified, documented, single-definition measures.
 * Experience designing layered data architectures that separate raw, conformed, and serving tiers, and knowing which transformations belong in which tier.
 * Experience exposing data and measures programmatically through APIs or query services for consumption by applications, services, or AI agents, not only by reporting tools.
 * Experience implementing data quality and observability practices, including reconciliation testing, data contracts, freshness and completeness monitoring, anomaly detection on pipelines, and end-to-end lineage.
 * Experience applying software engineering discipline to data work, including source control, CI/CD and deployment pipelines, environment separation, code review, and infrastructure as code.
 * Experience optimizing platform cost and performance through partitioning, incremental refresh, aggregation strategy, capacity management, and query tuning.
 * Familiarity with preparing data for AI and machine learning consumption, such as feature pipelines for model scoring, grounding and retrieval patterns for language models, embedding or vector stores, and ingestion of agent telemetry including identifiers, token usage, and evaluation output.
 * Experience partnering with data scientists to productionize models, and with business stakeholders to translate an operational question into a durable data design.
 * A platform mindset: a bias toward building reusable, documented capability rather than one-off extracts, and the judgment to know when a governed measure already answers the question.
 * Comfort operating with autonomy on a small team, owning a component end to end, and writing the documentation that lets someone else run it., * Masters Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 2+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR Bachelors Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 4+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR equivalent experience., * Masters Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 6+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR Bachelors Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 8+ years experience in data analysis and reporting, data science, business intelligence, or business and financial analysis OR equivalent experience.
 * Hands-on experience with Microsoft Fabric, Azure Synapse, Azure Data Factory, Databricks, or Power BI semantic models in a production environment.
 * Experience building data platforms that serve AI or machine learning workloads, including feature serving, retrieval and grounding data, or model scoring at scale.
 * Experience implementing data governance in practice, including certified metric definitions, tiered metric ownership, and the promotion process that moves a measure into executive reporting.
 * Experience supporting executive-facing analytics where accuracy, freshness, and traceability are non-negotiable.

Data Analytics IC4 - The typical base pay range for this role across the U.S. is USD $106,400 - $203,600 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $137,600 - $222,600 per year., Apache Spark, Application Programming Interface (API), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Business Analysis, Business Growth, Business Intelligence, Business Model, Business Operations, CSS (Cascading Style Sheet), Capacity Management, Capacity Strategy, Code Reviews, Computer Programming, Computer Science, Continuous Deployment/Delivery, Continuous Integration, Cost Control, Customer Experience, Customer Support/Service, Data Analysis, Data Management, Data Modeling, Data Quality, Data Recovery, Data Science, Dimensional Modeling, Documentation, Economics, Executive Assistant Skills , Financial Analysis, Financial Systems, Geography, Leadership, Machine Learning, Market Share, Marketing, Mathematics, Metrics, Microsoft Product Family, Microsoft Windows Azure, Modeling Languages, Multiplatform/Cross-Platform, Power BI, Problem Solving Skills, Product Engineering, Production Systems, Python Programming/Scripting Language, Query Optimization, Reconciliation,
Reporting Dashboards, Risk, SQL (Structured Query Language), Sales, Software Engineering, Source Code/Configuration Management (SCM), Statistics, Strategic Planning, Technical Support, Telemetry, Test Data, Testing, Traceability, Usage Analysis

## Description

You will design and operate the data pipelines that feed CSS intelligence, spanning both batch processing at organizational volume and near real-time streams where detection latency matters. That includes ingesting case and agent telemetry, integrating with the unified data platform and Finance systems, and building the scoring pipelines that run our data science models continuously in production rather than in a notebook. You will be accountable for the reliability of those pipelines: monitoring, alerting on your own infrastructure, handling schema drift and late-arriving data, and making sure a failure surfaces to you before it surfaces to a leader reading a brief.

You will build and own the semantic layer that sits between raw data and every consuming experience. This means dimensional models designed for the questions leaders actually ask, certified measures with documented definitions and clear ownership, consistent hierarchies across organization, product, offering, severity, and geography, and the security context that governs who sees what. Critically, you will design this layer to serve more than reports: our detection engine queries it for baselines and thresholds, and our executive experience queries it through a semantic API so that natural-language answers resolve to governed measures with a traceable query path rather than to improvised calculations.

You will make trust in the platform mechanical rather than aspirational. You will build data quality and observability into the pipelines themselves, including freshness stamps, reconciliation tests against sources of record, contract checks between producers and consumers, and lineage that lets anyone trace a number on a slide back to the rows behind it. You will bring engineering discipline to analytics through source control, deployment pipelines, environment separation, and infrastructure defined as code, and you will manage platform cost and performance deliberately through partitioning, incremental refresh, and query optimization.

You will work in close partnership with data scientists and applied AI engineers, turning experimental models into production services and preparing data for AI consumption, including the grounding, retrieval, and feature-serving patterns our agents depend on. You will also design for handoff: our intent is that patterns proven inside CSS can be adopted and operated more broadly, so schemas, APIs, deployment approaches, and documentation need to be legible to teams who did not build them., * Building the certified measure layer that our detection engine, executive brief, natural-language experience, and monthly review content all resolve to, so that no surface calculates its own number.

 * Designing the semantic API and query service that lets an AI agent answer a business question against governed measures under the askers security context, returning the answer with its filters, freshness stamp, and query trace.
 * Standing up continuous scoring pipelines that run case propensity and complexity models across CSS case volume and land the results where detection and workflow systems can act on them.
 * Ingesting AI agent telemetry, including agent identifiers, model and version, token consumption, and evaluation results, and modeling it so agent quality and cost can be compared across platforms.
 * Building the detection infrastructure that computes baselines, thresholds, and materiality gates on a schedule, and delivers validated signals into notification and workflow paths.
 * Implementing reconciliation and freshness monitoring so that a discrepancy between the platform and a source of record is caught and attributed automatically rather than discovered in a leadership review.
 * Establishing the deployment, versioning, and documentation patterns that allow a capability proven in CSS to be handed to a partner engineering team and operated at wider scale.


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