Job Responsibilities

- Responsible for leading a team of talented data engineers responsible for designing, building, and maintaining scalable data pipelines and infrastructure

- Work closely with cross-functional teams to ensure client data systems meet the highest standards of quality and performance

- Lead, mentor, and develop a team of data engineers, fostering a cooperative and inclusive team environment

- Conduct regular performance reviews, provide feedback, and set goals for team members

- Identify and address skill gaps, and provide opportunities for professional development

- Plan, execute, and deliver data engineering projects on time and within scope

- Coordinate with stakeholders to gather requirements, set priorities, and define project timelines.

- Ensure projects align with overall business objectives and data strategy

- Oversee the design, development, and maintenance of data pipelines, ETL processes, and data warehouse

- Ensure data quality, integrity, and security across all data engineering projects.

- Identify opportunities for process improvements and drive initiatives to enhance the efficiency and effectiveness of data operations.

- Has strong conceptual understanding of Data Warehousing and ETL, Data Governance and Security, Cloud Computing, and Batch & Real Time data processing

- Ability to build/drive reusable frameworks that can drive efficiency of the overall data system

- Has executed and lead multiple projects including on - streaming, batch, large data pipelines, etc.

- Manages conversation with the client stakeholders to understand the requirement and translate it into technical outcomes.

Required Tech Stack

- Strong experience with Databricks, Spark, and cloud platforms (Azure, AWS, GCP).





- Architect and deploy cloud-based data solutions (Azure, AWS, GCP).

- Define CI/CD strategies for data pipelines using Terraform, Azure DevOps, or GitHub Actions.

- Implement data cataloging, lineage tracking, and access control (Unity Catalog,Collibra, Alation).

- Ensure compliance with GDPR, CCPA, and industry-specific data security policies.

- Develop strategies for distributed computing, parallel processing, and caching mechanisms.

Required Non-Tech Stack

- Partner with data architects, product managers, and business leaders to define data requirements and align engineering efforts with business objectives.

- Define data engineering standards and playbooks to streamline development.

- Oversee end-to-end project execution, from scoping to delivery.

- Stay updated with emerging trends in data engineering, AI, and analytics to continuously improve architectures.

- Evaluate and recommend new data tools, frameworks, and best practices.

- Ability to translate complex technical concepts into business-friendly language.

- Excellent communication, leadership, and stakeholder management.

Good to Have Tech Stack

- Experience with machine learning and advanced analytics technologies

- Familiarity with data visualization tools and techniques

- Knowledge of data security and privacy practices

- Understanding of data governance and compliance frameworks

- Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes)

- Experience with graph databases and graph processing frameworks

- Experience with data virtualization and data federation techniques

- Proficiency in data profiling and data quality management

Preferred Educational Qualifications

B.E/B.Tech, MCA, M.Sc. (Mathematics, Statistics)

📌 Lead Data Engineer (Bengaluru)
🏢 MathCo
📍 Bengaluru