Key Responsibilities :

Technical Leadership :

- Lead, mentor, and guide a team of data engineers, driving best practices in data architecture, ETL/ELT development, and data pipeline management.

- Provide hands-on technical support and contribute to the design and development of data solutions, especially in high-stakes or complex scenarios.

- Collaborate with data architects, data scientists, and product managers to align engineering efforts with business objectives.

Data Engineering :

- Design, build, and maintain scalable data pipelines and ETL/ELT processes that deliver high-quality data for analytics, reporting, and business intelligence.

- Implement data integration and transformation processes using cloud and on-premise data tools (e.g., Snowflake, Databricks, Spark, AWS Glue, Azure Data Factory).

- Optimize performance, reliability, and scalability of data infrastructure and pipelines, ensuring data quality and security compliance.

Pre-Sales and Storytelling :

- Support pre-sales engagements, including scoping, designing solutions, and pitching data engineering capabilities to prospective clients.

- Translate complex technical concepts into easy-to-understand, value-driven narratives tailored to different stakeholders, from technical teams to C-level executives.

- Develop and deliver engaging presentations, proposals, and use cases that showcase data engineering solution impact on business outcomes.

Project Management

Experience :

- 10+ years of experience in data engineering, with at least 3 years in a leadership or team lead role.

- Proven experience in pre-sales, including proposal development, client presentations, and requirement scoping.

- Hands-on expertise with data engineering tools (e.g., SQL, Python, Spark, Snowflake, Databricks) and cloud platforms (AWS, Azure, GCP).

- Strong data storytelling and presentation skills, with the ability to adapt messaging to various audiences.

- Proficiency in PowerPoint and data visualization tools (e.g., Tableau, Power BI) to create compelling presentations.

- Solid understanding of data warehousing, data lakes, ETL/ELT processes, and data governance.

- Strong leadership and mentoring capabilities with a collaborative approach to team development.