nJob Responsibilities

n

n
- Responsible for leading a team of talented data engineers responsible for designing, building, and maintaining scalable data pipelines and infrastructure
n
- Work closely with cross-functional teams to ensure client data systems meet the highest standards of quality and performance
n
- Lead, mentor, and develop a team of data engineers, fostering a collaborative and inclusive team environment
n
- Conduct regular performance reviews, provide feedback, and set goals for team members
n
- Identify and address skill gaps, and provide opportunities for professional development
n
- Plan, execute, and deliver data engineering projects on time and within scope
n
- Coordinate with stakeholders to gather requirements, set priorities, and define project timelines.
n
- Ensure projects align with overall business objectives and data strategy
n
- Oversee the design, development, and maintenance of data pipelines, ETL processes, and data warehouse
n
- Ensure data quality, integrity, and security across all data engineering projects.
n
- Identify opportunities for process improvements and drive initiatives to enhance the efficiency and effectiveness of data operations.
n
- Has strong conceptual understanding of Data Warehousing and ETL, Data Governance and Security, Cloud Computing, and Batch & Real Time data processing
n
- Ability to build/drive reusable frameworks that can drive efficiency of the overall data system
n
- Has executed and lead multiple projects including on - streaming, batch, large data pipelines, etc.
n
- Manages conversation with the client stakeholders to understand the requirement and translate it into technical outcomes.
n

n

nRequired Tech Stack

n

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




- Architect and deploy cloud-based data solutions (Azure, AWS, GCP).
n
- Define CI/CD strategies for data pipelines using Terraform, Azure DevOps, or GitHub Actions.
n
- Implement data cataloging, lineage tracking, and access control (Unity Catalog,Collibra, Alation).
n
- Ensure compliance with GDPR, CCPA, and industry-specific data security policies.
n
- Develop strategies for distributed computing, parallel processing, and caching mechanisms.
n

n

nRequired Non-Tech Stack

n

n
- Partner with data architects, product managers, and business leaders to define data requirements and align engineering efforts with business objectives.
n
- Define data engineering standards and playbooks to streamline development.
n
- Oversee end-to-end project execution, from scoping to delivery.
n
- Stay updated with emerging trends in data engineering, AI, and analytics to continuously improve architectures.
n
- Evaluate and recommend new data tools, frameworks, and best practices.
n
- Ability to translate complex technical concepts into business-friendly language.
n
- Excellent communication, leadership, and stakeholder management.
n

n

nGood to Have Tech Stack

n

n
- Experience with machine learning and advanced analytics technologies
n
- Familiarity with data visualization tools and techniques
n
- Knowledge of data security and privacy practices
n
- Understanding of data governance and compliance frameworks
n
- Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes)
n
- Experience with graph databases and graph processing frameworks
n
- Experience with data virtualization and data federation techniques
n
- Proficiency in data profiling and data quality management
n

n

nPreferred Educational Qualifications

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

📌 Lead Data Engineer (Bengaluru)
🏢 MathCo
📍 Bengaluru