Senior Analytics Engineer

Sigma Systems

. Architect and lead scalable analytics data models and modern lakehouse data layers using dbt, SQL, and Python .

Posted 11/5/2026contractRemote • Massachusetts • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting scalable analytics data models and modern lakehouse data layers using SQL and Python, while leading complex data engineering projects and ensuring data quality and governance. Proven ability to mentor engineers and collaborate effectively with cross-functional teams to drive AI and analytics initiatives.

Highest-signal resume keywords

Expert-Level SQL ProgrammingPython Programming SkillsData Model DevelopmentData Pipeline OrchestrationData Governance Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Analytics EngineeringData EngineeringData TransformationDimensional ModelingData WarehousingFeature EngineeringMachine Learning PipelinesSemantic Layer DesignAutomated TestingCI/CD

Soft Skills

Strong Communication SkillsCollaboration SkillsMentoring SkillsTechnical Leadership

Tools & Technologies

DbtDagsterAWSAmazon S3Apache IcebergTrino

Industry Keywords

Data ArchitectureData QualityMetadata ManagementPrivacySecurityAI/ML InitiativesLarge Institutional Datasets

Tech Stack

Tools & technologies

ApacheAWSCloudPythonSQL

About the role

Key responsibilities & impact

Architect and lead scalable analytics data models and modern lakehouse data layers using dbt, SQL, and Python
Design, develop, and maintain enterprise data models and transformation pipelines for reporting, analytics, and AI applications
Define and govern semantic layers, enterprise metrics, business logic, and metadata management
Develop AI-ready data architectures and interfaces enabling LLMs and AI agents to interpret and query institutional data
Lead context engineering initiatives involving semantic models, metadata, MCP, and text-to-SQL capabilities
Design and implement production-grade data and ML pipelines, including feature engineering and embedding pipelines
Use Dagster and AWS to orchestrate, deploy, and manage scalable data workflows
Establish data engineering standards, data quality practices, automated testing, and software development best practices
Conduct advanced code reviews and ensure analytics solutions are reliable, scalable, and maintainable
Lead technical planning and delivery of cross-functional data and AI initiatives
Collaborate with data engineers, AI engineers, researchers, faculty, and business stakeholders
Mentor engineers and provide technical guidance
Contribute to long-term analytics engineering, data architecture, and AI strategy

Requirements

What you’ll need

Bachelor's degree in Computer Science, Information Systems, Data Science, Data Analytics, or a related technical field, or equivalent professional experience
8+ years of professional experience in Analytics Engineering, Data Engineering, or a related technical discipline
Demonstrated experience leading complex data engineering or analytics engineering projects
Expert-level SQL and Python programming skills
Extensive experience building data models, transformations, and analytics workflows using dbt or similar technologies
Strong knowledge of data architecture, dimensional modeling, data warehousing, and semantic layer design
Experience with modern cloud data platforms and lakehouse architectures
Strong understanding of Git, automated testing, CI/CD, code reviews, and data pipeline orchestration
Experience designing and maintaining production-grade data pipelines
Experience with data governance, data quality, metadata management, privacy, and security
Strong communication and collaboration skills, with the ability to work effectively with technical and non-technical stakeholders
Proven experience mentoring engineers and providing technical leadership
Preferred: experience with AWS cloud services, particularly Amazon S3
Preferred: hands-on experience with Apache Iceberg and Trino
Preferred: experience with dbt Core or dbt Cloud
Preferred: experience with Dagster or similar orchestration tools
Preferred: experience designing and implementing semantic layers and enterprise metrics frameworks
Preferred: experience supporting AI/ML initiatives involving LLMs, RAG, embeddings and vector-based search, text-to-SQL applications, AI agents, AI-ready data interfaces, and MCP
Preferred: experience with feature engineering and machine learning pipelines
Preferred: advanced degree in Computer Science, Data Science, Analytics, or a related discipline
Preferred: experience working with large institutional, research, or enterprise datasets

Benefits

Comp & perks

9-month contract opportunity
Remote work arrangement