Data Governance Manager

We don’t imagine a better future. We’re building it

2. Advanced Analytics, Machine Learning & AI

Nice to have:

Your Future With Us - Cosa troverai in KPMG

Unisciti a noi

We don’t imagine a better future. We’re building it

Would you like to do work that is truly meaningful and impactful? At KPMG, you will have the opportunity to help clients, society, and your colleagues address and solve today’s most current and complex challenges. KPMG Advisory provides consulting services to companies, supporting them in business model transformation, process improvement and digitalization, risk analysis and management, as well as extraordinary finance transactions.

Specifically, you will join the Lighthouse Team in Milan.

Lighthouse is the KPMG Advisory Centre of Excellence that delivers Data & Analytics solutions to the market.

You will be involved in delivering cutting-edge technology solutions, including algorithms, business intelligence, data visualization, advanced analytics, process mining and machine learning technologies to improve human decision-making.

More specifically, you will work within the Data & AI team and contribute to the design, implementation and production deployment of advanced solutions across the entire data and model lifecycle. In particular, you will be responsible for:

• Implementation and configuration of Data Catalog/Data Marketplace solutions, with experience in technologies such as Atlan, Zeenea, Collibra, Microsoft Purview, Alation, Informatica EDC/IDMC, DataHub and OpenMetadata.
• Metadata modelling and management, including Business Glossary, Business Terms, metadata models, ownership, stewardship, lineage, classification and certification.
• Supporting the design and implementation of Data Governance processes, translating roles, responsibilities, policies and processes into workflows and operational configurations within governance tools.
• Metadata management and Data Product lifecycle management, including the definition of domains, owners, input/output ports, SLA/SLOs, policies, lifecycle statuses and relations between assets, with knowledge of Data Mesh / Data Products approaches and, preferably, tools such as Atlan, Zeenea, DataHub, OpenMetadata and Witboost. For example, Zeenea currently provides specific capabilities for Data Product management and discovery.
• Configuration of workflows, roles, stewardship processes and application integrations through REST APIs, webhooks, Python and SQL, including integration with Identity & Access Management systems, such as Microsoft Entra ID/Azure AD, and with major Data Platform ecosystems.
• Data Contracts and metadata automation, with knowledge of the Open Data Contract Standard (ODCS), YAML/JSON, machine-readable contract management, and automation of metadata generation, validation and synchronization.
• Data Quality and Data Observability, with experience or knowledge of tools such as dbt, Soda and Great Expectations, to integrate rules, quality checks, control results and related metadata into the lifecycle of assets and Data Products.

Come As You Are – Your Skills and Characteristics

This opportunity is perfect for you if:

• You have a hybrid technical profile, with skills distributed across multiple areas of the Data & AI domain and different levels of depth depending on the area (engineering, analytics/AI, platforms), with the goal of continuing to grow.
• You have had at least one experience in technical management of Data & AI delivery projects, whether as a team lead, delivery lead or mentor/tutor for junior colleagues.
• You have gained at least 6 years of professional experience managing complex projects and technical teams.
• You have skills in the following areas:
• Strong knowledge of SQL and major databases and analytical engines (PostgreSQL, SQL Server, Snowflake, BigQuery).
• Experience designing and implementing ETL/ELT data pipelines and data modelling for analytical purposes.
• Knowledge of key data management principles, including data modelling, data quality, metadata & lineage, lakehouse architectures and cloud-native architectures.
• Familiarity with modern orchestration and pipeline frameworks (dbt, Apache Airflow, Azure Data Factory, AWS Glue, Fabric Pipelines).
• Experience with distributed environments and big data technologies (Apache Spark, Delta Lake, Parquet, Kafka / Pub-Sub).
• Experience integrating serving and visualization layers (Power BI, Qlik, Tableau, Looker) as an integral part of data pipelines and semantic layers.

2. Advanced Analytics, Machine Learning & AI

• Excellent knowledge of Python and the main libraries for data analysis, machine learning and data processing.
• Experience developing Machine Learning models