Data Expertise Manager - Customer Operations

Unilever Brasil Ltda.; Bengaluru; Híbrido; INR 4.000.000 - 7.000.000; Jornada completa

Descripción de la vacante

Unilever Brasil Ltda. in Bangalore, India, is seeking a Data Expertise Manager to lead a globally distributed team of Data SMEs, Data Engineers and analysts.

You will bridge Customer Operations with Data Foundation and product teams, building trusted data foundations for reporting, analytics and AI. The role combines people leadership with deep technical depth in Databricks, GCP, lakehouse architectures and modern data modelling, driving governance, quality, and scalable data products across

Formación

• 10+ years in data engineering, management or analytics in global organisations.
• Led India-based or globally distributed data teams incl. engineers and analysts.
• Hands-on depth in data platforms (lakehouse, cloud DW, ETL/ELT, pipelines).
• Expert data modelling across relational, dimensional, semantic and lakehouse.
• Strong stakeholder management across time zones with agile practices.
• Desirable: domain knowledge in Customer Operations or related areas.

Responsabilidades

• Lead and grow a globally distributed team of Data SMEs and engineers.
• Own end-to-end delivery from planning to post-go-live support.
• Translate business needs into data models, pipelines and data products.
• Enforce architecture, governance, data quality and security standards.
• Enable AI and analytics with trusted data foundations.
• Manage risk, capacity and vendor performance; drive cost/value balance.

Conocimientos

SQL

Spark SQL

Python

PySpark

Databricks

GCP

Azure

BigQuery

Data modeling

Data governance

Data quality

CI/CD

Data observability

Herramientas

Erwin

ER/Studio

PowerDesigner

Git

Azure DevOps

GitHub Actions

BigQuery

Databricks

Descripción del empleo

Job Title : Data Expertise Manager - Customer Operations

Location: Bangalore, India

Function: Customer Operations - Data Foundation

Scope: Global

Employment Type: Full-time, Hybrid

About the Role

Great customer operations run on great data. As our Data Expertise Manager, you will lead a globally distributed team of Data SMEs, Data Experts, Data Engineers, Business Analysts and partner colleagues, most of whom are based in Bangalore. Acting as the bridge between Customer Operations, Data Foundation, product teams, markets and delivery partners, you will build the trusted, well-governed and scalable data foundations behind our global Customer Operations, turning business priorities into reusable data products that are ready for reporting, analytics and AI. This is a genuinely hands-on leadership role for someone who enjoys combining people leadership with real technical depth.

What You Will Do

• Lead and grow the team: coach and develop your team with clear objectives and career growth, embedding business process knowledge into data design.
• Own delivery end to end: take Customer Operations data products and programmes from demand shaping and roadmap planning through build, release, post-go-live support (SLAs, incident management) and retirement.
• Provide hands-on technical leadership: translate business requirements into specifications, data models, pipelines and support models, and lead data analysis, profiling, modelling and solution design.
• Safeguard quality and compliance: review designs against enterprise standards for architecture, reuse, performance, resilience, security, privacy and governance, and drive automated data quality checks, reconciliation and monitoring.
• Enable AI and analytics: build AI-ready and analytics-ready foundations through trusted source integration, metadata, business definitions, semantic models and reusable consumption patterns.
• Partner and manage risk: align scope and priorities with Customer Operations, market, Data Foundation, platform, governance, product and partner teams, and resolve risks and issues with clear ownership.
• Manage capacity and cost: prioritise internal and third-party capacity and vendor performance, balancing speed, quality, cost and business value.
• Drive continuous improvement: champion simplification, standardisation, documentation and knowledge sharing, and build future-fit skills across engineering, cloud, governance, DevOps and AI enablement.

What You Will Bring

• Substantial experience (typically 10+ years, or equivalent demonstrable capability) in data engineering, data management, data architecture, analytics or BI, ideally in a global organisation, capability centre or shared services environment.
• Proven leadership of India-based or globally distributed data teams, including engineers, analysts, SMEs and third-party partners.
• Hands-on depth in modern data platforms (lakehouse, cloud data warehouse, ETL/ELT, orchestration, metadata, data quality and governance), including Databricks and GCP.
• Expert data modelling across relational, dimensional, semantic, operational and lakehouse patterns, for structured and semi-structured data.
• Strong stakeholder management across senior business, technology and vendor partners in multiple time zones, with delivery through Agile, Scrum, DevOps or product practices.
• Desirable: Customer Operations, Customer Development, trade promotion, order-to-cash, customer service, logistics or supply chain domain knowledge.

Technical Skills

• Programming: advanced SQL, Spark SQL, Python and PySpark for transformation, automation and testing.
• Databricks and lakehouse (essential): Databricks Workflows, Delta Live Tables, Apache Spark and Delta Lake, including medallion architecture, partitioning, schema evolution, change data capture (CDC), slowly changing dimensions and performance tuning.
• Google Cloud Platform (essential): BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub and Cloud Composer, plus integration across Azure and GCP.
• Azure: Azure Data Factory, ADLS Gen2 and Synapse or equivalent.
• Pipelines and integration: batch and incremental pipelines ingesting from SAP and other ERPs, APIs, files and third-party sources (JSON, Parquet, CSV), with orchestration, monitoring and alerting.
• Engineering practice: Git, code reviews, CI/CD (Azure DevOps or GitHub Actions), automated data testing, data observability and cloud cost optimisation.
• Data modelling (essential): conceptual, logical and physical models (dimensional, relational, Data Vault and semantic) using tools such as Erwin, ER/Studio or PowerDesigner, and reusable data products with clear data contracts.
• Governance and security: Unity Catalog, Microsoft Purview or Google Dataplex for cataloguing and lineage; role-based access, data masking and data protection compliance.
• Desirable: streaming (Spark Structured Streaming, Event Hubs, Kafka), infrastructure-as-code (Terraform, Bicep, Databricks Asset Bundles), Power BI semantic models, and data preparation for machine learning