With the growth in the Corporate Finance Technology Team acquisitions and organizational changes ongoing, we need to increase our capabilities in the FinTech Team especially around Data Lake technologies and within the Data Lake team by seeking a FinTech Data Assets Model Senior Analyst.
The FinTech Data Lake Data Assets Model Senior Analyst owns the enterprise finance data lake required to run the business, ensuring the integrity, reliability and governance of Mars Inc.'s financial data through sound policies, processes and effective management of platform and data changes. The role supports the data infrastructure that ingests, consolidates and curates financial data from source systems across segments, regions and units, covering actuals on a periodic, year-to-date and MAT basis. The role is responsible for maintaining the data lake platform so the businesses can generate regular performance reporting and ad-hoc insights through analytics.
Key Responsibilities:
Build and maintain the finance data lake platform as an integral part of management reporting
Provide user support for data lake consumers across analytics and reporting tools
Create and manage the data pipelines and processes that support period close and plan submissions
Build and maintain and update data models, schemas and hierarchies in line with changing business needs
Ensure the data lake and associate systems like OneStream, CFIN, PAPM are aligned, with consistent data definitions and reconciled outputs
Work closely with the platform and technical support teams (e.g., data engineering, cloud infrastructure, source system owners)
Create and manage access provisioning and data governance, including role-based permissions and catalog controls
Maintain and update technical documentation, data dictionaries and user training materials
Perform period-end close activities, including data loads, validations and reconciliations
Participate in data restatements, working alongside other teams to ensure financial data integrity is maintained
Support FinTech approved cross platform and cross segment projects from a technical standpoint
Drive data quality improvement initiatives and the (re-)enforcement of data quality roles and responsibilities
The successful candidate must be able to communicate across multiple levels in a broad variety of circumstances to be effective. Interactions include: MDG, OneStream, 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:
Core Azure Data Platform
Azure Data Lake Storage Gen2 (ADLS): hierarchical namespace, ACLs, lifecycle management, storage tiering
Azure Databricks: 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: 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
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