We are seeking an accomplished Data Engineer to join our rapidly growing team. This role is responsible for designing, building, and evolving scalable data pipeline architecture to ensure reliable, high-quality data delivery across the organization. The ideal candidate is a hands-on engineer with strong experience building and maintaining data pipelines, and a passion for delivering robust data solutions that enable analytics and business decision-making.
The Data Engineer will partner with data architects, data analysts, data scientists, and cross-functional stakeholders to deliver trusted data assets supporting a wide range of business initiatives. They will ensure efficient and reliable data delivery across multiple teams, systems, and products in a dynamic environment. This role offers the opportunity to evolve and enhance a modern data platform by improving existing pipelines or redesigning them for greater scalability, performance, and maintainability. The successful candidate will apply modern software engineering practices, including AI-assisted development tools, to improve productivity, code quality, and delivery speed while maintaining strong engineering standards.
RESPONSIBILITIES
- Design, develop, and maintain scalable data pipelines and data products for
internal and external consumers.
- Build and optimize batch and near real-time data ingestion, transformation, and
delivery processes.
- Integrate data from internal and external sources to support business, reporting,
and analytics requirements.
- Collaborate with data architects, analysts, data scientists, and business
stakeholders to deliver scalable data solutions and support Sisense dashboards
and analytics assets.
- Design and implement data models that support reporting, analytics, and
operational use cases.
- Ensure data quality, reliability, and performance through monitoring, validation,
automated testing, and troubleshooting.
- Write maintainable, well-documented, and testable code; participate in code
reviews; and leverage AI-assisted development tools to improve quality and
efficiency.
- Support CI/CD, infrastructure automation, technical documentation, and
continuous improvements to data architecture, tooling, and engineering practices
QUALIFICATIONS
- 2–4 years of professional experience in Data Engineering, Data Warehousing, or
related roles.
- Strong hands-on experience with Python and SQL for building scalable data
pipelines and transformation logic.
- Experience with Apache Spark, Parquet, and Azure Databricks, including
Databricks workflows, Delta Lake, Delta Sharing, and Unity Catalog.
- Strong SQL expertise including performance tuning, indexing, partitioning, query
optimization, and stored procedure development.
- Solid understanding of ETL/ELT methodologies, data warehousing principles,
and modern data engineering best practices.
- Experience designing and implementing data models to support analytics,
reporting, and operational use cases.
- Experience supporting or working with BI tools such as Sisense (or similar
platforms).
- Experience with CI/CD pipelines and version control practices (e.g., GitLab,
Jenkins, or equivalent).
- Experience working in fast-paced product environments with an emphasis on
delivery, maintainability, and minimizing technical debt.
- Strong communication skills with the ability to collaborate across technical and
non-technical stakeholders
BONUS QUALIFICATIONS
- Experience building lightweight data applications or internal tools using any of
the following frameworks such as Streamlit, Dash, Flask, Gradio, Shiny, or
Node.js.
- Ability to navigate ambiguity, prioritize effectively, and adapt to changing
business needs.
- Prior experience in financial services or regulated environments is a plus