Data Engineer

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