Data Engineering Manager

Data Engineering ManagerEducation

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

Experience

1114 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.