Manager Data Engineering, ITC

Job description

**Who You’ll Work With** You will be part of Nike’s Global Technology organization, working within the India Tech Centre in Bangalore, India to support Consumer Product & Innovation capabilities. You will report to the Engineering Director and partner closely with product managers, principal engineers, architects, data engineers, data science, security, platform and business stakeholders. You will lead a team of data engineers and collaborate with local and global teams to deliver reliable, scalable and secure data platforms that enable analytics, reporting, AI/ML and business decision-making. **Who We Are Looking For** We are looking for an experienced Data Engineering Manager to lead, coach and grow a high-performing engineering team in Bengaluru. In this role, you will own the strategy, architecture and execution of enterprise data platform capabilities that power analytics, reporting, AI/ML and business decision-making. You will combine technical depth with people leadership, delivery ownership and strong cross-functional collaboration. The ideal candidate has proven experience building production-grade data pipelines, modern cloud data platforms and data governance practices, while developing engineers and partnering with stakeholders to deliver measurable business outcomes. **What You’ll Work On** As Manager, Data Engineering, you will lead the design, build and operation of enterprise-scale data platforms, including lakehouse, data warehouse, ingestion, transformation, orchestration and integration capabilities. You will guide the team in delivering reliable batch and real-time data pipelines, improving data quality and observability, enabling AI/ML-ready data products, and driving engineering best practices such as automation, testing, monitoring, documentation and CI/CD. You will also manage priorities, delivery cadence, technical roadmap, resource planning and stakeholder alignment across local and global teams. **Key Responsibilities** - Lead, mentor, recruit and grow a high-performing team of data engineers, fostering a culture of technical excellence, collaboration and continuous improvement. - Define and execute the technical roadmap for enterprise data platform capabilities, aligning priorities with product, architecture and business strategy. - Design, build and operate scalable, fault-tolerant data pipelines and ETL/ELT frameworks that support batch, streaming and near-real-time data processing. - Architect and evolve data lakehouse, data warehouse, ingestion, transformation and integration layers using modern cloud-native technologies. - Oversee data quality, observability, metadata, governance, privacy and security standards across platform components and data products. - Partner with product management, software engineering, analytics, data science, architecture, security and business stakeholders to understand needs and deliver analytics-ready data solutions. - Enable AI/ML-ready data architecture, including reusable data products, feature engineering workflows and reliable data services for advanced analytics. - Drive DataOps and engineering best practices including CI/CD, automated testing, monitoring, alerting, documentation, performance optimisation and cost efficiency. - Manage backlog prioritisation, sprint planning, delivery cadence, stakeholder communication and operational stability for the data platform team. - Evaluate, recommend and implement new tools, frameworks and technologies that improve platform reliability, scalability, developer productivity and business value. **Qualifications Required** - Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics or a related technical field, or equivalent practical experience. - 10+ years of hands-on experience in data engineering, including experience building and operating production-grade pipelines and data platforms at scale. - 3+ years of people leadership experience, including hiring, coaching, mentoring, performance management and development of technical teams. - Strong proficiency in SQL, Python and distributed data processing frameworks such as Spark or PySpark. - Deep expertise in data warehousing, lakehouse architectures, data modelling, ETL/ELT design and large-scale data integration patterns. - Experience with cloud data platforms and services such as AWS, Snowflake, Databricks or equivalent technologies. - Experience with orchestration, transformation and streaming technologies such as Apache Airflow, dbt, Kafka or Kinesis. - Solid understanding of data governance, metadata management, data cataloguing, privacy, security, data quality and observability practices. - Experience enabling analytics and AI/ML use cases through reliable data products, feature engineering workflows and reproducible data pipelines. - Excellent problem-solving, communication and stakeholder management skills, with the ability to translate technical concepts for non-technical audiences. **Preferred** - Experience working in a globally distributed engineering organisation and partnering with stakeholders across regions. - Hands-on experience with data mesh, data product thinking, feature stores, real-time analytics platforms or modern lakehouse architectures. - Familiarity with infrastructure-as-code, containerisation and platform engineering practices such as Terraform, CloudFormation, Docker or Kubernetes. - Experience managing cloud infrastructure usage, platform reliability, performance optimisation and cost efficiency. - Familiarity with BI, dashboarding, semantic modelling and analytics engineering practices.