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 collaborative 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
Robust 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 (Vasanth Nagar)
🏢 MathCo
📍 Vasanth Nagar