Mars

Global confectionery, food, and petcare company

Data Assets Model Senior Analyst

Data Management +1Confirmed live in the last 24 hours

Full-timeWarsaw, PolandNo salary listed

About the Job

Requirements

Knowledge of Azure Data Lake Storage Gen2, including hierarchical namespace, ACLs, lifecycle management, and storage tiering.
Knowledge of Azure Databricks workspace administration, cluster policies, job and workflow orchestration, Databricks SQL, and cost and performance tuning.
Knowledge of Delta Lake, including ACID transactions, time travel, schema evolution and enforcement, and Z-ordering.
Knowledge of Unity Catalog metastore design, catalogs, schemas, volumes, row- and column-level security, data lineage, external locations, and storage credentials.
Knowledge of Azure Blob Storage containers, access tiers, SAS and managed identity access, and private endpoints.
Knowledge of Azure Data Factory and/or Synapse Pipelines, including pipeline design, parameterization, integration runtimes, triggers, and metadata-driven ingestion frameworks.
Knowledge of Azure SQL Database, Managed Instance, and/or Synapse dedicated SQL pools, including serving layer design, indexing, and partitioning.
Knowledge of medallion architecture and lakehouse design patterns.
Knowledge of dimensional modeling, data vault, and finance-specific modeling, including general ledger, hierarchies, consolidation, and foreign exchange.
Knowledge of batch and incremental/CDC ingestion, and streaming with Structured Streaming, Event Hubs, or Kafka.
Knowledge of PySpark and Spark SQL, including performance tuning, partitioning strategy, broadcast joins, skew handling, and caching.
Advanced SQL skills, including window functions, common table expressions, recursive queries, query plan analysis, and performance optimization.
Python for data engineering; Scala is a plus.
Knowledge of Delta Live Tables/Lakeflow declarative pipelines and Databricks Workflows.
Knowledge of data migration and modernization from legacy platforms such as Essbase and on-premises data warehouses.
Knowledge of data governance and cataloging, including Unity Catalog and Microsoft Purview.
Knowledge of role-based and attribute-based access control, Microsoft Entra ID groups, and SCIM provisioning.
Knowledge of managed identities, service principals, Azure Key Vault, and secrets management.
Knowledge of network security, including VNet injection, private link, firewall, and NSG configuration.
Knowledge of data quality frameworks such as Great Expectations, Delta expectations, and custom reconciliation rules.
Knowledge of data lineage, audit logging, retention, compliance, SOX, and financial controls.
Knowledge of master data and reference data management, including MDG integration.
Knowledge of Azure DevOps repositories, boards, YAML pipelines, release management, and branching strategies.
Knowledge of CI/CD for Databricks and ADF, including Databricks Asset Bundles and ARM/Bicep templates.
Knowledge of infrastructure as code using Terraform, Bicep, or ARM.
Knowledge of Git-based workflows, code review standards, and automated testing for data pipelines.
Knowledge of monitoring and observability using Azure Monitor, Log Analytics, alerting, and pipeline SLAs.
Knowledge of FinOps and cost management for Databricks and Azure resources.
Knowledge of Agile/Scrum/SAFe delivery, backlog management, and sprint planning in Azure DevOps.
Ability to provide technical design authority through architecture reviews, standards, and reusable frameworks.
Ability to manage vendors and partners, including statements of work and deliverable oversight.
Knowledge of incident, problem, and change management aligned with ITIL for close-critical workloads.
Ability to communicate with stakeholders ranging from engineers to senior finance leadership and across multiple organizational levels and circumstances.

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 data pipelines and processes that support period close and plan submissions.
Build, maintain, and update data models, schemas, and hierarchies in line with changing business needs.
Ensure the data lake and associated systems, including OneStream, CFIN, and PAPM, are aligned with consistent data definitions and reconciled outputs.
Work closely with platform and technical support teams, including data engineering, cloud infrastructure, and 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 with other teams to maintain financial data integrity.
Support approved FinTech cross-platform and cross-segment projects from a technical standpoint.
Drive data quality improvement initiatives and reinforce data quality roles and responsibilities.
Mentor data engineers and analysts and conduct code reviews.
Manage vendor and partner statements of work and deliverables.
Manage incidents, problems, and changes for close-critical workloads.

Desired Qualifications

Familiarity with Microsoft Fabric and OneLake.
Experience with REST APIs and integration with enterprise finance systems for consolidation, planning, or master data management.

Job Description:

Data Assets Model Senior Analyst (f/m/x)

in Mars Global Services

Location: Warsaw (Poland)

Hybrid working (up to 50%)

* Fully virtual recruitment experience *

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
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)
Batch and incremental/CDC ingestion; streaming with Structured Streaming, Event Hubs or Kafka
PySpark and Spark SQL: performance tuning, partitioning strategy, broadcast joins, skew handling, caching
Advanced SQL: window functions, CTEs, recursive queries, query plan analysis, performance optimization
Python for data engineering, plus Scala as a plus
Delta Live Tables / Lakeflow declarative pipelines and Databricks Workflows
Data migration and modernization from legacy platforms (e.g., Essbase, on-prem warehouses)

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
Network security: VNet injection, private link, firewall and NSG configuration
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
Vendor and partner management, including SOW and deliverable oversight
Incident, problem and change management (ITIL-aligned) for close-critical workloads
Stakeholder communication from engineers to senior finance leadership

*Depending on location, benefits may be slightly different*

Apply now and upload you profile in English.

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About the Company

Mars

Global confectionery, food, and petcare company

Mars makes confectionery, food, and petcare products and also operates pet health services through networks like Banfield, VCA, BluePearl, and Royal Canin. Its products are marketed under familiar brands such as M&M’s, Snickers, Milky Way, Skittles, Dove, Orbit, Pedigree, Royal Canin, Whiskas, and Cocoavia, sold globally to consumers and pet owners. The company differs from competitors by combining a broad, diversified portfolio with a global, family-owned structure guided by the Mars Principles (Quality, Responsibility, Mutuality, Efficiency, Freedom), which drives its operations and partnerships. Mars aims to help communities prosper and to grow its business while improving social and environmental outcomes, using its scale to support responsible practices across the world.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $27B
Headquarters: McLean, VA
Founded: 1911

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