Data Engineer II

DriveWealth•New York, NY

3d•$145,000 - $165,000•Hybrid

About The Position

DriveWealth is seeking 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) and Semantic Data Layer (creating curated, consistent, and reliable datasets that enable analytics, reporting, and decision making).

Requirements

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.
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

Nice To Haves

Fintech or capital market experience
Familiarity with regulatory and compliance-driven reporting (e.g. FINRA, GDPR)
Experience supporting or mentoring junior team members.

Responsibilities

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 modeling, 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.