Data Expertise Manager – Customer Operations

Overview

Lead a globally distributed Customer Operations data team, based mainly in Bangalore, and own the trusted, well-governed data foundations behind global Customer Operations, from roadmap through post-go-live support.

What you'll 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'll need

• 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.
• 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.
• 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.
• Certifications (essential): Databricks Certified Data Engineer (Associate or Professional); Google Cloud Professional Data Engineer or Professional Cloud Architect; and a data modelling or data management certification such as DAMA CDMP, or equivalent demonstrable expertise.
• Education: bachelor's or master's degree in Computer Science, Engineering, Information Systems, Data Engineering, Business Analytics, Operations Research or a related discipline; equivalent practical experience will also be considered.

Nice to have

• Customer Operations, Customer Development, trade promotion, order-to-cash, customer service, logistics or supply chain domain knowledge.
• 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 and generative AI.
• Azure Data Engineer or Fabric Data Engineer Associate; data governance, Agile (SAFe, PSM) or product management certifications.

Details

• Location: Bangalore, India.
• Employment type: Full-time, hybrid.
• Scope: Global.