Data Engineer WSnowflake

Role-1

Senior Data Engineer

Contract Length: 6 months extensions

Location: Burbank CA or Glendale CA

Preferred location: Glendale

Must work onsite hybrid starting week 1 (2 weeks max from the time of offer acceptance)

Zero exceptions will be made as this is corporate policy - no delayed remote start no remote work

2 days a week onsite must be flexible up to 4 days a week onsite

The assignment is expected to run through the end of the year (approximately 6-7 months) with a potential opportunity for extension depending on project needs and phases.

The team is building a new Snowflake data platform focused primarily on financial data ingestion and modeling.

Strong hands-on experience with Snowflake especially building data pipelines designing and structuring data objects creating data marts

Experience working with financial datasets (e.g. financial planning actuals) was emphasized as highly valuable though not strictly mandatory.

The role is intended for a senior-level data engineer not a junior or lightly experienced Snowflake resource.

Depth in Snowflake is more important than total years as a data engineer.

o A candidate with only minimal Snowflake exposure (e.g. short-term experience) would not be ideal.

Key Responsibilities

Design and Build Data Pipelines: Develop automate and maintain scalable data pipelines to bring together financial and operational data (GL vendor invoices headcount budgets) from multiple enterprise sources into Snowflake.

Model and Structure Data: Partner with architects to develop robust data models and create semantic layers enabling user- friendly flexible reporting and analysis.

Implement Cost Allocation Logic: Write performant SQL and/or Python code that automates multi-layer allocation engines cost pool processing and service-level analytics.

Collaborate Across Borders: Work closely with product managers finance teams AI/ML engineers and QA to deliver governed high-quality solutions on schedule.

Automate and Optimize: Leverage workflow/orchestration tools (such as Snowflake Tasks DBT or Airflow) and adopt CI/CD/code versioning best practices for reliability and speed.

Ensure Data Quality & Auditability: Implement and maintain reconciliation processes checks and IT controls to meet Disney s high standards for trust compliance and transparency.

Support and Evolve: Provide operational and incident support performance tuning and assist with continuous improvement of Enterprise Data & Analytics infrastructure.

Basic Qualifications

7 years of experience as a Data Engineer with emphasis on cloud data platforms (Snowflake strongly preferred).

Expertise in advanced SQL for data transformation & modeling (e.g. window functions CTEs).

Working experience with Python for ETL/ELT automation or analytics.

Hands-on experience delivering robust maintainable data pipelines for complex enterprise environments.

Prior experience developing financial cost models (Financial planning financial spend cost allocation & recovery etc.)

Familiarity with workflow orchestration tools (DBT Airflow etc.).

Knowledge of financial accounting or technology operations systems and data such as SAP Clarity ServiceNow Cognos Planning and other similar systems.

Demonstrated ability to work collaboratively in cross-functional teams.

Strong communication skills; experience writing documentation and partnering with non-technical partners

Preferred Qualifications

Familiarity with Generative AI tools such as Snowflake Cortex AI

Knowledge and experience working with Technology Business Management (TBM) framework and methodology

Strong understanding of corporate finance & accounting processes

Role-2

Tech stack is SQL and Spark AWS cloud.

Only 3-4 Years of experience

Entry level opportunity Good pay

Data Engineer

Contract Duration: 12 months extensions

Location: Glendale

Onsite Requirement

Current requirement: 2 days per week onsite

Potential future increase to up to 4 days but not confirmed

Must work onsite hybrid starting week 1 (2 weeks max from the time of offer acceptance)

Zero exceptions will be made as this is corporate policy - no delayed remote start no remote work

2 days a week onsite must be flexible up to 4 days a week onsite

Core Responsibilities

The candidate will be expected to:

Work directly on data pipelines

Debug and resolve pipeline issues

Support testing use cases

Contribute to production code with peer review oversight

Focus on code quality and reliability

Must-Have Skills

The must-have requirements have been refined:

Spark experience (must-have)

SQL experience (must-have)

Preferred / secondary:

Databricks (preferred not required)

AWS (preferred cloud platform)

Cloud & Technical Expectations

AWS is the preferred cloud environment

Multi-cloud experience is acceptable at the junior level but less scrutiny is applied compared to senior roles

Less emphasis on architectural reasoning for junior candidates

Focus is on practical hands-on experience rather than theoretical decision-making

More hands-on pipeline troubleshooting and maintenance

Ability to identify and resolve pipeline issues using basic to mid-level methods (ABC methods)

Less emphasis on advanced architecture or large-scale system design

The role is now more execution-focused with the expectation that candidates will:

Be overseen and mentored by senior team members

Participate in peer code reviews focused on quality and testing

Grow into the role over time

Candidate Profile

Ideal candidates (3-year level) are expected to be:

Early in their career / recently in the workforce

Highly adaptable and fast learners

Strongly hands-on and execution-focused

Comfortable troubleshooting real production issues

Less focused on organizational or architectural decision-making