Python Data Analysis Internship Jobs in Salinas, CA

Are you an experienced, passionate pioneer in data and technology who wants to work in a collaborative environment? As an experienced Senior Data Product Analyst - Data Management Engineer III, you will have the opportunity to share new ideas and collaborate on projects as a consultant without the extensive demands of travel. If so, consider an opportunity with Deloitte under our Project Delivery Talent Model. Project Delivery Model (PDM) is a talent model tailored specifically for long-term, onsite client service delivery.

Recruiting for this role ends on: September 10th, 2026

Work you'll do / Responsibilities

You will support a Data & Analytics Foundry operating across numerous business product teams in a scaled staff augmentation model, serving as a senior data product and analytics resource across the Media Network analytics framework.

• Lead the identification, documentation, and prioritization of gaps in existing data products that cause delays, inefficiencies, inconsistent measurement, or limitations for business users.
• Translate identified gaps into a centralized intake process, prioritized backlog, delivery roadmap, and actionable resolution plans.
• Partner across pods, business product teams, domain data leaders, product managers, engineers, analysts, data scientists, and business stakeholders to align priorities, dependencies, and delivery timelines.
• Coordinate shared resources and cross-functional contributors to address data product issues efficiently and effectively.
• Act as a senior cross-pod connector to drive representation, alignment, decision-making, and consensus across product, data, engineering, analytics, and business teams.
• Elevate point solutions into scalable, reusable, governed, and discoverable data products that improve measurement consistency and reduce duplication.
• Define and maintain data product requirements, user needs, product objectives, success metrics, adoption KPIs, acceptance criteria, and operational performance indicators.
• Conduct product experiments, stakeholder interviews, user research, and feedback analysis to better understand data consumption patterns, user needs, and feature requirements.
• Prioritize enhancements and product features based on business value, user demand, feasibility, dependencies, risk, and expected impact.
• Document and maintain data product roadmaps, release plans, product requirements, operational support requirements, and product documentation.
• Analyze and measure product performance, data usability, data quality, adoption, discoverability, accessibility, and business value to inform product decisions.
• Partner with engineering, QA, analytics, and business stakeholders to deliver iterative improvements and validate product readiness.
• Apply advanced SQL to query, analyze, profile, and validate data; investigate data issues; and assess data product quality and performance.
• Use Python, as appropriate, to support data analysis, automation, validation, data quality checks, and product insights.
• Apply knowledge of data modeling, BI, semantic layers, and metrics layers to support effective data product design and analytics enablement.
• Support the design and adoption of reusable data products, governed data services, semantic layers, metrics layers, data models, and structured data services.
• Support governance, metadata, cataloging, lineage, access control, privacy, security, and documentation standards to improve transparency, usability, and trust in data products.
• Define and maintain common business definitions, data elements, metrics, ownership models, and quality expectations across business product teams.
• Support the delivery of trusted data products for BI reporting, dashboards, advanced analytics, machine learning, generative AI, and other data-driven use cases.
• Collaborate with data engineering teams using modern data engineering patterns and orchestration technologies such as Spark, Airflow, dbt, or equivalent tools.
• Lead backlog refinement, sprint planning, prioritization, dependency management, release coordination, and delivery tracking for assigned data product workstreams.
• Coordinate work across onshore and offshore delivery teams, including shared resources supporting the Data & Analytics Foundry operating model.
• Prepare product roadmaps, requirements documentation, process flows, status reports, demonstrations, executive briefings, and stakeholder-ready narratives.
• Translate complex data architecture, data engineering, analytics, and AI concepts into clear business requirements, product strategies, and implementation priorities.
• Build consensus across stakeholders when data definitions, product priorities, implementation approaches, or delivery timelines differ.
• Provide clear guidance to others.
• Mentor analysts and other delivery professionals on data product management, data analysis, data quality, governance, and stakeholder engagement.
• Communicate regularly with Engagement Managers, project team members, and representatives from various functional and / or technical teams, including escalating any matters that require additional attention and consideration from engagement management.
• Independently and collaboratively lead client engagement workstreams focused on improving analytics capabilities, resolving delivery bottlenecks, and driving operational outcomes.
• Meticulous attention to detail and quality of work product.
• Ability to build and sustain professional relationships.
• Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment.
• Strong interpersonal skills and professional demeanor.
• Ability to meet deadlines.

The Team

AI & Data - AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated solutions across software, data, AI, networks, and cloud infrastructure. These solutions are powered by engineering for business advantage, helping transform mission-critical operations. Our teams enable clients to modernize technology and data platforms while delivering scalable, high-value outcomes tailored to their business needs.

Qualifications

Required

• Bachelor's degree in computer science, Engineering, Data Science, Information Systems, Analytics, or a related field, or equivalent practical experience.
• 6+ years of experience across data product management, data product analysis, data engineering, analytics engineering, BI/reporting, data science, data governance, or related technical roles.
• 3+ years of experience supporting data product, AI/ML, generative AI, advanced analytics, or data-driven transformation initiatives.
• 6+ years of experience working with data products, data analysis, data engineering, analytics, or related data and technology capabilities.
• Advanced proficiency in SQL, including complex joins, common table expressions, window functions, data profiling, data validation, and analytical query development.
• Experience with data engineering patterns and at least 1 processing, transformation, or orchestration technology such as Spark, Airflow, dbt, or equivalent.
• Demonstrated experience building, managing, or delivering enterprise data products, semantic layers, metrics layers, data models, governed APIs, or reusable structured data services.
• Working knowledge of data governance, metadata management, data cataloging, data lineage, data quality, access control, privacy, security, and compliance practices.
• Experience gathering, analyzing, and documenting business, technical, and data product requirements.
• Experience defining product success metrics, user adoption KPIs, acceptance criteria, operational indicators, and measurable business outcomes.
• Experience evaluating data usability, quality, discoverability, accessibility, timeliness, and business value.
• Limited immigration sponsorship may be available.
• Ability to travel up to 10%, on average, based on project and client needs.

Preferred

• Experience using Python for data analysis, automation, data validation, data quality, or data engineering use cases.
• Experience with BI and reporting platforms, enterprise dashboards, governed metrics, or self-service analytics.
• Working knowledge of knowledge graphs, graph-based data models, entity resolution, or relationship-oriented data products.
• Experience with cloud data platforms and modern data ecosystems, such as Snowflake, Databricks, Microsoft Azure, AWS, or Google Cloud.
• Experience with data catalog, governance, metadata, or data quality platforms such as Collibra, Alation, Informatica, Microsoft Purview, or equivalent tools.
• Experience supporting AI/ML or generative AI solutions through trusted datasets, governed data products, reusable features, or responsible data practices.
• Experience working in cross-functional product, analytics, engineering, and QA environments.
• Experience conducting product experiments, user research, stakeholder interviews, or market research.
• Experience maintaining data product roadmaps, release plans, product backlogs, and operational support models.
• Agile delivery experience, including backlog management, sprint planning, release planning, and dependency tracking.
• Experience working in a staff augmentation, delivery center, shared services, or product-aligned operating model.
• Ability to manage multiple priorities and stakeholders with minimal supervision.

Benefits

At Deloitte, we know that great people make a great organization. We value our people and offer employees a broad range of benefits. Learn more about what working at Deloitte can mean for you.

Recruiting tips
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Our people and culture
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Our purpose

Deloitte's purpose is to make an impact that matters for our people, clients, and communities. At Deloitte, purpose is synonymous with how we work every day. It defines who we are. Our purpose comes through in our work with clients that enables impact and value in their organizations, as well as through our own investments, commitments, and actions across areas that help drive positive outcomes for our communities. Learn more.

Professional development
From entry-level employees to senior leaders, we believe there's always room to learn. We offer opportunities to build new skills, take on leadership opportunities and connect and grow through mentorship. From on-the-job learning experiences to formal development programs, our professionals have a variety of opportunities to continue to grow throughout their career.

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Are you an experienced, passionate pioneer in data and technology who wants to work in a collaborative environment? As an experienced Senior Data Product Analyst - Data Management Engineer III, you will have the opportunity to share new ideas and collaborate on projects as a consultant without the extensive demands of travel. If so, consider an opportunity with Deloitte under our Project Delivery Talent Model. Project Delivery Model (PDM) is a talent model tailored specifically for long-term, onsite client service delivery.

Recruiting for this role ends on: September 10th, 2026

Work you'll do / Responsibilities

You will support a Data & Analytics Foundry operating across numerous business product teams in a scaled staff augmentation model, serving as a senior data product and analytics resource across the Media Network analytics framework.

• Lead the identification, documentation, and prioritization of gaps in existing data products that cause delays, inefficiencies, inconsistent measurement, or limitations for business users.
• Translate identified gaps into a centralized intake process, prioritized backlog, delivery roadmap, and actionable resolution plans.
• Partner across pods, business product teams, domain data leaders, product managers, engineers, analysts, data scientists, and business stakeholders to align priorities, dependencies, and delivery timelines.
• Coordinate shared resources and cross-functional contributors to address data product issues efficiently and effectively.
• Act as a senior cross-pod connector to drive representation, alignment, decision-making, and consensus across product, data, engineering, analytics, and business teams.
• Elevate point solutions into scalable, reusable, governed, and discoverable data products that improve measurement consistency and reduce duplication.