Data Engineer, DriveWealth

• 2-3 years of professional experience in data engineering or related fields, with a track record of building and contributing to data pipelines and platforms
• Technical Skills:
• Proficiency in Python and SQL
• Familiarity with Spark (PySpark and SparkSQL) and distributed data processing
• Familiarity with orchestration and transformation tools (e.g. Airflow, DBT)
• Familiarity with Databricks and cloud platforms (AWS preferred)
• Familiarity with infrastructure as code (e.g.Terraform or equivalent)
• Working knowledge in shell scripting and command line tooling (e.g. Databricks CLI)
• Proven ability to work independently and cross-functionally with product, engineering and business teams
• Experience working in teams with CI/CD pipelines, Git-based workflows and Agile methodologies
• Curiosity, accountability and a drive to improve data quality, scalability and usability across the company
• BS in Computer Science or equivalent

Desirable

• We are looking for a Data Engineer who thrives on solving data problems, and enjoys working cross-functionally with product, engineering and analytics teams to deliver trusted, high-impact data solutions
• The ideal candidate is excited to take ownership of well-defined data challenges and support the evolution of our data platform
• The Data & Analytics organization at DriveWealth powers the company’s data ecosystem end-to-end
• Within it, the Data Platform Engineering team owns two critical pillars:
• Data Ingestion - building reliable pipelines that bring data from diverse internal and external sources
• Semantic Data Layer - creating curated, consistent and reliable datasets that enable analytics, reporting and decision making
• Contribute to the design, build and maintenance of robust data pipelines (batch, streaming and replication) that power analytics, reporting and compliance use-cases
• Translate business needs into data solutions in collaboration with product managers, engineering teams and business stakeholders
• Proactively monitor and address challenges and opportunities, helping to anticipate scaling, performance and quality needs before they become blockers
• Adhere to and promote best practices, including documentation, code reviews and standards for data modelling, orchestration and tooling
• Actively participate in code reviews and knowledge sharing, raising the bar for data engineering craft and helping grow team capabilities
• Support the contribution to self-service data products, enabling data consumers with curated datasets, semantic layers and tools that accelerate insight generation