Manager Data Engineering, ITC

Nike; India; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Nike's India Tech Centre in Bengaluru is seeking an experienced Data Engineering Manager to lead and grow a high-performing team. You will own the strategy, architecture and execution of enterprise data platform capabilities powering analytics, AI/ML and business decisions.

You will guide design, build and operation of lakehouse, data warehouse, ingestion and integration layers, enabling reliable batch and real-time data pipelines, data quality and observability.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Mathematics or a related field, or equivalent practical experience.
• 10+ years of hands-on data engineering experience building pipelines and platforms at scale.
• 3+ years of people leadership experience.
• Strong proficiency in SQL, Python and distributed data processing frameworks such as Spark or PySpark.
• Deep expertise in data warehousing, lakehouse architectures, data modelling and ETL/ELT patterns.
• Experience with cloud data platforms and services such as AWS, Snowflake, Databricks or equivalent.
• Experience with orchestration, transformation and streaming technologies like Airflow, dbt, Kafka or Kinesis.
• Understanding of data governance, metadata, privacy and security best practices.
• Experience enabling analytics/AI use cases through reliable data products and pipelines.
• Excellent problem-solving and stakeholder management skills.

Responsibilities

• Lead, mentor and grow a high-performing data engineering team.
• Define and execute the technical roadmap for enterprise data platform capabilities.
• Design, build and operate scalable data pipelines and ETL/ELT frameworks across batch and real-time processing.
• Architect and evolve lakehouse, data warehouse, ingestion and integration layers using cloud-native tech.
• Oversee data quality, observability, governance, privacy and security standards.
• Partner with product management, analytics, data science and security stakeholders to deliver analytics-ready data solutions.
• Enable AI/ML-ready data architecture with reusable data products and features.
• Drive DataOps, CI/CD, testing, monitoring and cost efficiency across the platform.
• Manage backlog, sprint planning and stakeholder communication for platform reliability.
• Evaluate and adopt new tools/tech to improve platform reliability and developer productivity.

Skills

Data pipelines

SQL

Python

Spark / PySpark

Airflow

Data warehousing

Leadership

Cloud platforms

Education

Bachelor's or Master's in CS/Engineering

Tools

Snowflake

Databricks

AWS

Kubernetes

dbt

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.