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