Data Engineering Lead - Snowflake/Matillion/dbt
Location: Hyderabad
Experience: 7 to 10 Years
Experience
- 7 to 10 years of experience in data engineering with hands-on expertise in data pipeline development, architecture, and system optimization.
- Proven track record in leading data engineering teams and managing end-to-end project delivery.
Technical Skills
- Expertise in programming languages such as Python, or Scala.
- Proficiency in designing and delivering data pipelines in Cloud Data Warehouses (e.g., Snowflake, Redshift), using various ETL/ELT tools such as Matillion, dbt, Striim, etc.
- Solid understanding of database systems (relational and NoSQL) and data modeling techniques.
- Hands-on experience of 2+ years in designing and developing data integration solutions using Matillion and/or dbt. o Strong knowledge of data engineering and integration frameworks.
- Expertise in architecting data solutions.
- Successfully implemented at least two end-to-end projects with multiple transformation layers.
- Good grasp of coding standards, with the ability to define standards and testing strategies for projects.
- Proficiency in working with cloud platforms (AWS, Azure, GCP) and associated data services.
- Enthusiastic about working in Agile methodology.
- Possess a comprehensive understanding of the DevOps process including GitHub integration and CI/CD pipelines.
- Experience working with containerization (Docker), and orchestration tools (such as Airflow, Control-M).
Job Description
We are looking for an accomplished and dynamic Data Engineering Lead to join our team and drive the design, development, and delivery of cutting-edge data solutions. This role requires a balance of strong technical expertise, strategic leadership, and a consulting mindset. As the Lead Data Engineer, you will oversee the design and development of robust data pipelines and systems, manage and mentor a team of 5 to 7 engineers, and play a critical role in architecting innovative solutions tailored to client needs.
You will lead by example, fostering a culture of accountability, ownership, and continuous improvement while delivering impactful, scalable data solutions in a fast-paced, consulting environment.
Key Responsibilities
- Data Solution Design and Development:
- Architect, design, and implement end-to-end data pipelines and systems that handle largescale, complex datasets.
- Ensure optimal system architecture for performance, scalability, and reliability.
- Evaluate and integrate new technologies to enhance existing solutions.
- Implement best practices in ETL/ELT processes, data integration, and data warehousing.
- Project Leadership and Delivery:
- Lead technical project execution, ensuring timelines and deliverables are met with high quality.
- Collaborate with cross-functional teams to align business goals with technical solutions.
- Act as the primary point of contact for clients, translating business requirements into actionable technical strategies.
- Team Leadership and Development:
- Manage, mentor, and grow a team of 5 to 7 data engineers.
- Conduct code reviews, validations, and provide feedback to ensure adherence to technical standards.
- Provide technical guidance and foster an environment of continuous learning, innovation, and collaboration.
- Optimization and Performance Tuning:
- Analyze and optimize existing data workflows for performance and cost-efficiency.
- Troubleshoot and resolve complex technical issues within data systems.
- Adaptability and Innovation:
- Embrace a consulting mindset with the ability to quickly learn and adopt new tools, technologies, and frameworks.
- Identify opportunities for innovation and implement cutting-edge technologies in data engineering. '
- Exhibit a "figure it out" attitude, taking ownership and accountability for challenges and solutions.
- Client Collaboration:
- Engage with stakeholders to understand requirements and ensure alignment throughout the project lifecycle.
- Present technical concepts and designs to both technical and non-technical audiences.
- Communicate effectively with stakeholders to ensure alignment on project goals, timelines, and deliverables.
- Learning and Adaptability:
- Stay updated with emerging data technologies, frameworks, and tools.
- Actively explore and integrate new technologies to improve existing workflows and solutions.
- Internal Initiatives and Eminence Building:
- Drive internal initiatives to improve processes, frameworks, and methodologies.
- Contribute to the organization’s eminence by developing thought leadership, sharing best practices, and participating in knowledge-sharing activities.
Skills: etl,devops,aws,ci/cd,data modeling,dbt,airflow,docker,github,elt,data engineering,striim,matillion,control-m,redshift,gcp,azure,scala,snowflake,integration,data solutions,python,nosql
