Data Engineering Manager – India

Job Description

Data Engineering Manager

Education

· Bachelor’s degree in Engineering (BE), Technology (B.Tech), or an equivalent field.

Experience

· 11–16 years of overall experience in data engineering and software development, with strong exposure to modern data platforms.

· Minimum 3+ years of experience managing data engineering projects and teams, with accountability for delivery, quality, and outcomes.

· Strong hands-on background earlier in the career as a Java Developer or Data Developer working on modern technology stacks.

· Experience building and delivering data solutions using cloud-native technologies rather than legacy-only ETL stacks such as SSIS or DataStage.

· Exposure to or hands-on experience with cloud platforms such as AWS, Azure, and/or Databricks is a significant plus.

· Proven ability to operate at the intersection of technology, delivery, and people leadership.

Key Expertise – Technical Skills

· Strong foundational experience in modern data engineering stacks, including distributed data processing frameworks and scalable data pipelines.

· Solid hands-on background in Java and/or Python, with practical experience designing production-grade data processing applications.

· Experience working with Spark-based or equivalent distributed data processing technologies, particularly in cloud environments.

· Exposure to cloud-based data platforms on AWS, Azure, or Databricks, including understanding of how data pipelines are built, deployed, and operated at scale.

· Good understanding of modern data architectures such as data lakes, lakehouse architectures, and analytics platforms.

· Working knowledge of CI/CD practices, version control, and automated deployment strategies for data engineering solutions.

· Strong understanding of data security fundamentals, including access control, secure data handling, and compliance-aware design.

Architecture & Design

· Ability to understand, review, and guide solution- and platform-level data architectures designed by architects and senior engineers.

· Strong grasp of data modeling concepts, including normalized models, dimensional modeling, and analytics-oriented schemas.

· Experience guiding teams on design trade-offs, scalability considerations, and non-functional requirements.

· Ability to ensure consistent application of architectural standards and best practices across multiple data initiatives.

· Appreciation of cloud architecture considerations, including scalability, reliability, and cost awareness.

Data Engineering & Analytics Delivery

· Strong understanding of end-to-end data pipeline delivery, including ingestion, transformation, validation, and consumption.

· Experience managing delivery of ETL / ELT pipelines for structured and semi-structured data at scale.

· Familiarity with analytical workloads and how engineered datasets support reporting, dashboards, and downstream analytics.

· Ability to oversee performance, reliability, and data quality aspects of data engineering solutions in production environments.

· Experience managing delivery in cloud-based data ecosystems is a strong advantage.

Project & People Management

· Proven experience managing data engineering projects, including planning, estimation, execution, and delivery tracking.

· Strong people management experience, including mentoring, performance management, and capability development of data engineers.

· Ability to balance hands-on technical involvement with managerial responsibilities based on project needs.

· Experience working in Agile delivery environments, collaborating closely with product owners, architects, and stakeholders.

· Strong ability to manage dependencies, risks, and delivery commitments across multiple workstreams.

Continuous Improvement & Modern Practices (Good to Have)

· Exposure to modern practices such as cloud-native development, automation, DevOps for data, and platform standardization.

· Interest in improving engineering productivity through better tooling, processes, and development practices.

· Awareness of emerging trends in data engineering and analytics, with the ability to assess relevance pragmatically.

Responsibilities

· Manage and lead data engineering teams delivering modern, cloud-based data solutions.

· Own delivery of data engineering projects, ensuring timely execution, quality, and alignment with requirements.

· Act as a bridge between architecture, engineering teams, and stakeholders, ensuring clarity of expectations and outcomes.

· Review technical designs and implementations to ensure adherence to architectural guidelines and best practices.

· Provide hands-on guidance and mentorship to engineers, especially in complex technical areas.

· Ensure data engineering solutions meet defined non-functional requirements such as performance, reliability, security, scalability, and cost awareness.

· Drive continuous improvement in team processes, delivery practices, and technical maturity.

Skills & Competencies

· Strong leadership and people management skills with a collaborative mindset.

· Solid technical judgment to guide teams without micromanaging implementation.

· Ability to communicate effectively with both technical and non-technical stakeholders.

· Strong ownership mindset with accountability for delivery outcomes.

· Ability to operate effectively in fast-paced, evolving environments.

Skills to be evaluated on: Data Engineering

Mandatory Skills: Data Engineering

 

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