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