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Cloud Adoption Engineer - Oracle Government, Defense & Intelligence
Oracle
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Cloud Adoption Engineer - Oracle Government, Defense & Intelligence
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Black & Veatch
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Mars Corporate

Data Asset Model Senior Engineer

United States

The pay range shown is applicable to all US locations listed within the job posting:USD 101,120.00 - USD 139,040.00

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Job details

Pay: The pay range shown is applicable to all US locations listed within the job posting:USD 101,120.00 - USD 139,040.00
Apply by: Nov 15, 2026
Posting language: English
Posted: Nov 5, 2026

Original job description

Job Description:

With continued growth in the Corporate Finance Technology (FinTech) team, coupled with ongoing finance initiatives, acquisitions, and organizational change, we are seeking a FinTech Data Asset Model Senior Engineer to strengthen our capabilities in Azure Data Lake technologies within the FinTech Data Lake team. The candidate will engineer, operate, and optimize the finance data lake, ensuring the integrity, reliability, and governance of Mars Inc.'s financial data through effective control of platform, data, and process changes. The candidate is hands-on in ingesting, consolidating, transforming, and curating financial data across segments, regions, and business units, enabling periodic and ad hoc reporting, analytics, and business insights. The candidate will design, build, and deliver corporate data solutions, driving transparency of financial performance through integrated systems, data pipelines, data models, and reporting that inform business decisions across the enterprise. The candidate will architect solutions, develop data assets, write, test, deploy, and maintain production-grade PySpark, SQL, and Python solutions and data pipelines, troubleshoot complex issues, optimize performance, perform code reviews, establish engineering standards, and mentor engineers, vendor staff, and analysts. The role ensures trusted financial data and analytics are delivered on time and analyzes and interprets financial results to identify KPI variances and long-term performance trends. The candidate will play a critical role in ensuring timely financial reporting by building, operating, monitoring, and enhancing the data pipelines and data loads required for Period Close and Plan submissions. Strong ownership, business acumen, and problem-solving skills are essential to diagnose and resolve technical, financial, functional, and process-related issues.

Key Responsibilities:

Operate and enhance the finance data lake platform as an integral part of management reporting
Diagnose and resolve data platform, pipeline, and consumer issues across analytics and reporting tools
Build, monitor and optimize the data pipelines and processes that support period close and plan submissions
Perform code reviews and manage Git-based code integration, branching, and merge activities with other team members.
Design, maintain and evolve data models, schemas and hierarchies in line with changing business needs
Align and reconcile the data lake and associated systems such as OneStream, CFIN and PAPM, ensuring consistent data definitions and outputs
Collaborate with platform and technical support teams (e.g., data engineering, cloud infrastructure, source system owners) to resolve issues and deliver enhancements
Execute data restatements, working alongside other teams to maintain financial data integrity
Implement and govern access controls, permissions, and data governance
Develop and maintain technical documentation, data dictionaries and user training materials
Execute month-end financial close activities, including data loads, validations and reconciliations
Lead technical design and hands-on delivery of FinTech-approved cross-platform and cross-segment solutions
Implement and drive data quality improvements and the (re-)enforcement of data quality roles and responsibilities

Context and Scope:

The ideal candidate is a hands-on technical leader with 7-10+ years of data engineering experience, including 5+ years of hands-on Azure Databricks, Spark/PySpark, and Delta Lake experience. The successful candidate should have strong experience building and supporting data products using Spark/PySpark, Python, advanced SQL, Databricks Workflows, Unity Catalog, and modern lakehouse architectures. The role requires a proven track record of strong problem-solving skills, independent technical decision-making, and a solid understanding of finance data and reporting processes. Success in this role requires direct involvement in solution design, development, optimization, mentoring, troubleshooting, and delivery, combined with strong collaboration, communication, and a focus on data quality. Experience with ERP systems, cloud data platforms, and data engineering best practices is essential, along with the ability to quickly learn and apply new technologies. The successful candidate must be able to communicate across multiple levels in a broad variety of circumstances to be effective. Interactions include:

Internal systems like MDG, SFO, One Consolidation, Data Lake and CFIN/PAPM teams
Data engineering, cloud platform and source system owners
External partners and vendors
Process and workstream leads on project initiatives

Skillset and Technologies experience:

7-10 years overall data engineering / analytics engineering experience
5+ years of hands-on Azure Databricks, Spark/PySpark, and Delta Lake experience
5+ years of advanced SQL development
5+ years of Python development for data engineering
3+ years working with modern lakehouse/data governance capabilities (Unity Catalog, data governance, security, metadata management)

Detailed Azure skills:

Azure Data Lake Storage Gen2 (ADLS): hierarchical namespace, ACLs, lifecycle management, storage tiering
Azure Databricks: Advanced Notebook and PySpark development, workspace administration, cluster policies, job and workflow orchestration, Databricks SQL, cost and performance tuning
Delta Lake: ACID transactions, time travel, schema evolution and enforcement, Z-ordering
Unity Catalog: Enterprise metastore design, catalogs/schemas/volumes, row- and column-level security, data lineage, external locations and storage credentials
Azure Blob Storage: containers, access tiers, SAS and managed identity access, private endpoints
Azure Data Factory (ADF) and/or Synapse Pipelines: pipeline design, parameterization, integration runtimes (including self-hosted), triggers, metadata-driven ingestion frameworks
Azure SQL Database / Managed Instance / Synapse dedicated SQL pools: serving layer design, indexing, partitioning

Data Engineering and Architecture

Medallion architecture (bronze/silver/gold) and lakehouse design patterns
Dimensional modeling (star/snowflake), data vault, and finance-specific modeling (GL, hierarchies, consolidation, FX)
PySpark and Spark SQL: advanced development, performance tuning, partitioning strategy, broadcast joins, skew handling, caching, and workload optimization
Advanced SQL: CTEs, window functions, recursive queries, query plan analysis, performance optimization
Advanced Python for data engineering
Delta Live Tables / Lakeflow declarative pipelines and Databricks Workflows
Data migration and modernization from legacy platforms or within Azure (e.g., Essbase, OneStream, on-prem warehouses, AOH to DNA)
Auto Loader and incremental ingestion patterns for large-scale data processing
AI-assisted engineering (Databricks Assistant, Mosaic AI)

Governance, Security and Quality

Data governance and cataloging (Unity Catalog, Microsoft Purview)
Role-based and attribute-based access control, Microsoft Entra ID (Azure AD) groups, SCIM provisioning
Managed identities, service principals, Azure Key Vault, secrets management
Data quality frameworks (e.g., Great Expectations, Delta expectations, custom reconciliation rules)
Data lineage, audit logging, retention and compliance (SOX, financial controls)
Master data and reference data management (MDG integration)

DevOps, Automation and Delivery

Azure DevOps (ADO): repos, boards, pipelines (YAML), release management, branching strategies
CI/CD for Databricks and ADF (Databricks Asset Bundles, ARM/Bicep templates)
Infrastructure as Code: Terraform, Bicep or ARM
Git-based workflows, code review standards, automated testing for data pipelines
Monitoring and observability: Azure Monitor, Log Analytics, alerting, pipeline SLAs
FinOps and cost management for Databricks and Azure resources
Microsoft Fabric / OneLake familiarity (a plus)
REST APIs and integration with enterprise finance systems (consolidation, planning, MDG): Nice to have

Delivery and Leadership

Agile/Scrum/SAFe delivery, backlog management and sprint planning in ADO
Technical design authority: architecture reviews, standards, reusable frameworks
Mentoring and code review for data engineers and analysts
Incident, problem and change management (ITIL-aligned) for close-critical workloads
Stakeholder communication from engineers to senior finance leadership

What You Can Expect From Mars

Work with talented associates in a collaborative and purpose-driven environment guided by the Five Principles of Mars.
Make a visible impact on a world-class manufacturing operation.
Access industry-leading learning and development opportunities through Mars University.
Build your career within one of the world's most recognized food and pet care companies.
Competitive compensation, annual incentive opportunity, and comprehensive benefits package.

The base pay range for this position at commencement of employment is between the range listed below, however, base pay offered may vary depending on multiple individualized factors, including but not limited to job-related knowledge, skills, and experience. The total compensation package for this position will include variable pay, medical and dental benefits, participation in 401k plan, and paid time off benefits. Details of participation in these benefit plans will be provided if an applicant receives an offer of employment.

The pay range shown is applicable to all US locations listed within the job posting:USD 101,120.00 - USD 139,040.00

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More about this job

Skills

Reporting
Data models
PySpark
SQL
Python
Troubleshoot
Financial reporting
Problem solving
Git
Technical support
Data engineering
Documentation
Account reconciliation
Apache Spark
Databricks
Unity
Decision making
Coaching
Troubleshooting
Collaboration
Communication skills
ERP
Microsoft Azure
ACLS
Workflow
SAS
Microsoft Azure Data Factory
Oracle Essbase
Process data
Cataloging
Regulatory compliance
Data management
DevOps
CI/CD
Infrastructure as code
IBM Terraform
Test automation
Google Analytics
Cost management
API development

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