Develop and maintain efficient ETL (Extract, Transform, Load) pipelines that ingest, process and transform large datasets from diverse sources. Build and manage data storage solutions using relational and non-relational databases, ensuring high availability, performance and scalability.
Work with APIs, third-party data sources and internal databases to create unified datasets that provide actionable insights for business stakeholders.
Partner with business intelligence analysts, data scientists and software engineers to design and implement data-driven solutions that meet business requirements.
Design optimized data schemas and structures that support analytical workloads and improve query performance.
Leverage orchestration tools like Apache Airflow or Prefect to automate ETL workflows and improve operational efficiency.
Provide technical guidance, review code and promote best practices within the data engineering team.
Lead and mentor Data and AI Engineers, providing technical direction, coaching, and engineering guidance.
Lead solution architecture and technical design, ensuring alignment with business objectives, product priorities, and engineering standards.
Design and optimize scalable data pipelines, cloud data platforms, data models, and storage solutions supporting analytical and operational workloads.
Architect reliable batch and real-time data processing solutions, incorporating data quality, governance, security, performance, and scalability.
Partner with Product Owners, business stakeholders, Architects, Analysts, and Engineering teams to translate requirements into sustainable data solutions.
Take end-to-end technical ownership across design, development, deployment, production reliability, and continuous improvement.
Evaluate technical options and make informed architecture decisions considering performance, scalability, security, cost, reliability, and maintainability.
Establish engineering and operational practices covering CI/CD, automation, testing, observability, monitoring, release readiness, and production support.
Lead complex production incident resolution, root-cause analysis, preventive actions, and reliability improvements, while guiding engineers in troubleshooting and reducing operational risk.
Drive engineering excellence through architecture/design/code reviews, reusable patterns, DevOps practices, and AI-assisted development.
Strong product mindset, connecting technical decisions and engineering investments to business and customer outcomes.
Proven ability to lead, mentor, and influence engineers while remaining technically hands-on.
Strong analytical, problem-solving, troubleshooting, and technical decision-making skills.
Effective communication and stakeholder management across technical, business, and product teams.
Ability to navigate ambiguity, balance competing priorities, and proactively manage risks, dependencies, and trade-offs.
Strong commitment to engineering excellence, operational reliability, and continuous improvement.
Willingness to provide technical leadership during critical production incidents, major releases, and business-critical situations outside regular working hours when required.