Senior Analytics Engineer

firstPRO US; Boston (MA); On-site; USD 140,000 - 230,000; Full time

Job summary

firstPRO US seeks a Senior Analytics Engineer to design and build scalable analytics data architecture, semantic models, and data pipelines powering enterprise reporting, advanced analytics, and AI-driven solutions.

The role sits at the intersection of analytics engineering, data engineering, and AI. It emphasizes quality, governance, and reusable data products, guiding other engineers without direct people management responsibilities.

Qualifications

• Experience designing analytics-ready data models and data products.
• Strong knowledge of semantic layers and AI-ready data engineering.
• Experience with data quality, governance, and observability.

Responsibilities

• Architect, design, and lead scalable analytics data models and products.
• Design enterprise data models and lakehouse architectures using dbt, SQL, and Python.
• Develop data solutions leveraging cloud storage and modern query technologies like AWS S3, Iceberg, and Trino.
• Establish modeling patterns and standards for consistency, scalability, and performance.
• Solve complex data architecture, modeling, and scalability challenges.
• Partner with data architects and data engineers on integration patterns and technical design decisions.

Skills

Analytics engineering

Data modeling

DBT

SQL

Python

Cloud data

Tools

dbt

SQL

Python

AWS S3

Apache Iceberg

Trino

Job description

The Senior Analytics Engineer will lead the design and development of modern analytics data architecture, semantic models, and data pipelines that power enterprise reporting, advanced analytics, machine learning, and AI-enabled solutions.

This role sits at the intersection of analytics engineering, data engineering, data architecture, and artificial intelligence. The Senior Analytics Engineer will apply strong software engineering principles to build scalable, trusted, and reusable data products while establishing the semantic and contextual foundations required for accurate analytics, AI applications, and machine learning workflows.

The Senior Analytics Engineer will serve as a technical leader on complex, high-priority initiatives, establish engineering and data quality standards, and partner closely with data engineering, AI/ML engineering, architecture, and business stakeholders. This position does not have formal people-management responsibilities but will provide technical leadership, mentorship, and guidance to other engineers.

Essential Functions

• Architect, design, and lead the development of scalable, analytics-ready data models and data products.
• Design enterprise data models and modern lakehouse architectures using dbt, SQL, and Python.
• Develop data solutions leveraging cloud storage and modern query technologies such as AWS S3, Apache Iceberg, and Trino.
• Establish modeling patterns and standards that promote consistency, reusability, scalability, and performance.
• Solve complex data architecture, modeling, and scalability challenges.
• Partner with data architects and data engineers on integration patterns, platform architecture, and technical design decisions.

2. Semantic Layer & Context Engineering – 25%

• Lead the design and implementation of semantic layers that connect enterprise data to users, applications, analytics platforms, and AI systems.
• Define enterprise metrics, business logic, relationships, and metadata to establish trusted sources of truth.
• Design context engineering strategies incorporating semantic models, metadata, knowledge graphs, retrieval strategies, and other AI-ready data structures.
• Develop data interfaces that enable AI applications, including text-to-SQL, LLM-powered analytics, and emerging standards such as MCP.
• Establish evaluation frameworks to measure the accuracy, reliability, and trustworthiness of AI-driven analytics.
• Design and deliver production-grade data pipelines supporting analytics, machine learning, and AI applications.
• Architect and optimize batch and event-driven pipelines using modern orchestration tools and cloud services.
• Develop pipelines supporting feature engineering, model training and scoring, embedding generation, RAG, and AI analytics.
• Build reliable, scalable data processing workflows that support both operational and analytical use cases.
• Implement monitoring, alerting, error handling, and lifecycle management for production data pipelines.

4. Engineering Standards, Quality & Governance – 10%

• Establish and champion software engineering standards for analytics and data engineering.
• Define best practices for version control, automated testing, CI/CD, documentation, and code review.
• Conduct expert-level code and architecture reviews.
• Implement data quality frameworks, data contracts, lineage, observability, and monitoring.
• Ensure data solutions align with organizational requirements for governance, privacy, security, compliance, and responsible AI.
• Provide technical leadership for complex, cross-functional data and analytics initiatives.
• Partner with data engineers, AI/ML engineers, architects, analysts, and business stakeholders to translate organizational priorities into scalable data products.
• Lead technical planning, architecture discussions, and delivery efforts.
• Mentor engineers through design reviews, code reviews, and knowledge-sharing activities.
• Evaluate emerging technologies, tools, and engineering practices and recommend improvements to the organization's analytics and AI capabilities.
• Help establish the long-term technical roadmap for analytics engineering and AI-ready data infrastructure.