Senior Data Engineer

Pyramid Consulting, Inc.; Chandler (AZ); Hybrid; USD 77,000 - 110,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Paid sick leave

Job summary

Pyramid Consulting, Inc. in Chandler, AZ is seeking a Senior Data Engineer for an 18+ months contract in a hybrid work setup. You will design and maintain scalable data pipelines, develop AI-ready data models, and implement data governance to ensure accuracy and security across the enterprise.

The role emphasizes Spark, Python, and data quality, with collaboration across product, architecture, and governance teams to deliver reliable data assets for AI-driven initiatives.

Qualifications

• 5+ years of Spark experience
• 5+ years of total Data Engineer experience
• 3+ years of Data Modelling experience
• Experience in financial services is a plus

Responsibilities

• Design, develop, and maintain scalable data pipelines to ingest, process, transform, and integrate data from multiple sources using Python, SQL, ETL/ELT frameworks.
• Build reliable data processing workflows to support AI, analytics, automation, and enterprise applications.
• Develop and maintain data models, storage architectures, and data transformation frameworks for AI/GenAI consumption.
• Ensure data governance, metadata management, data quality, lineage, and accessibility across enterprise data.
• Support RAG, embeddings, vector databases, semantic search, and context retrieval use cases.
• Establish reusable data engineering patterns, documentation standards, and best practices while monitoring data health and pipeline performance.

Skills

Spark

Python

Data Modelling

Data Quality

Job description

Immediate need for a talented SeniorData Engineer. This is a 18+ months contract opportunity with long-term potential and is located in Chandler, AZ (Hybrid). Please review the job description below and contact me ASAP if you are interested.

Job ID:26-34069

Pay Range: $68/hour. Employee benefits include, but are not limited to, health insurance (medical, dental, vision), 401(k) plan, and paid sick leave (depending on work location).

Key Responsibilities:

• Data Engineering & Pipeline Development: Design, develop, and maintain scalable data pipelines to ingest, process, transform, and integrate structured and unstructured data from multiple enterprise sources using Python, SQL, ETL/ELT frameworks, and data integration technologies.
• Build reliable data processing workflows to support AI, analytics, automation, and enterprise applications.
• Data Architecture & AI-Ready Data Preparation: Develop and maintain data models, storage architectures, and data transformation frameworks to organize and prepare enterprise data for AI/GenAI consumption.
• Work with operational documentation, network configurations, technical standards, policies, logs, telemetry, and runbooks to create structured, searchable, and reusable knowledge assets.
• Support RAG, embeddings, vector databases, semantic search, and context retrieval use cases.
• Data Governance & Metadata Management: Implement data governance standards and frameworks covering data quality, metadata management, data lineage, data ownership, stewardship, data classification, retention, and lifecycle management.
• Establish standards for data definitions, documentation, traceability, and accessibility to ensure enterprise data is accurate, consistent, secure, and suitable for downstream AI applications.
• Data Quality, Validation & Monitoring: Develop automated data quality checks, validation rules, data profiling, monitoring frameworks, and preventative/detective controls to ensure data accuracy, completeness, consistency, and freshness.
• Identify upstream data issues, perform root cause analysis, implement remediation strategies, and continuously optimize data pipelines and transformation processes.
• Cross-Functional Collaboration & Continuous Improvement: Collaborate with engineering, architecture, product, network operations, and governance teams to understand business and technical requirements and translate operational knowledge into reliable data assets.
• Establish reusable data engineering patterns, documentation standards, and best practices while monitoring data health, pipeline performance, governance compliance, and overall readiness of data for AI-driven solutions.

Key Requirements and Technology Experience:

• Key Skills: Spark, Python, Data Modelling, Data Quality
• Financial services
• 5+ Years of Spark
• 5+ years of total Data Engineer experience
• 3+ years of Data Modelling experience

Our client is a leading Financial Industry, and we are currently interviewing to fill this and other similar contract positions.

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