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

Mars; Sochaczew; Hybrid; PLN 180,000 - 240,000; Full time

Job summary

Mars Global Services in Warsaw is seeking a Data Assets Model Senior Analyst to own the enterprise finance data lake, ensuring data integrity, governance and timely reporting across regions.

You will work with Azure Data Platform tools to build data pipelines, maintain data models, and collaborate with data engineering, finance and IT teams to deliver reliable analytics and ad-hoc insights for decision-makers.

Qualifications

• Experience designing and managing enterprise data lakes for finance data
• Knowledge of data governance frameworks and data lineage
• Strong SQL and Python/Scala for data engineering workflows
• Familiarity with cloud data platforms and Azure stack

Responsibilities

• Build and maintain the finance data lake platform as part of management reporting
• Provide user support for data lake consumers across analytics and reporting tools
• Create and manage data pipelines supporting period close and plan submissions
• Build and maintain data models, schemas and hierarchies to meet changing business needs
• Ensure alignment with OneStream, CFIN, PAPM and other systems and reconciled outputs
• Collaborate with data engineering, cloud infrastructure and source system owners
• Manage access provisioning, data governance and catalog controls
• Maintain technical documentation and data dictionaries
• Perform period-end close data loads, validations and reconciliations
• Support data restatements with other teams to ensure data integrity
• Drive data quality initiatives and enforcement of data quality roles

Skills

Azure Data Lake Storage Gen2

Azure Databricks

Delta Lake

Unity Catalog

Azure Blob Storage

ADF / Synapse Pipelines

Azure SQL Database

Medallion architecture

Dimensional modeling

Batch processing

PySpark

Python

Data governance

Data quality

Azure DevOps

Databricks Workflows

Azure Monitor

Tools

Azure DevOps

Databricks

Job description

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

in Mars Global Services

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*