Onsite, ML FinTech Data Engineer (Bethesda Maryland)
$65 - $67 per hourAddison Group
Position Title: ML Data Engineer
Location: Bethesda, MD - 5 Days onsite - No Relocation
Assignment Type: Contract to Hire
Pay Rate: $65.00 - $67.00
Work Schedule: Onsite, Monday - Friday
Benefits: This position is eligible for medical, dental, vision, and 401(k).
Work Authorization: Must be authorized to work in the United States. This position is not eligible for sponsorship.
Job Description Addison Group is partnering with a high-growth boutique tactical asset management firm headquartered in Bethesda, MD that is investing heavily in modern data and AI capabilities to sharpen their competitive edge. They are looking to bring on a ML Data Engineer to play a critical role in building and scaling their data infrastructure from the ground up.
This is an opportunity for someone who thrives in a fast-paced startup environment, enjoys owning their work end to end, and wants real upside growth potential in a firm that is building something differentiated in the financial services space. If you are the type of engineer who wants to build, not maintain this is your role
Key Responsibilities
Location: Bethesda, MD - 5 Days onsite - No Relocation
Assignment Type: Contract to Hire
Pay Rate: $65.00 - $67.00
Work Schedule: Onsite, Monday - Friday
Benefits: This position is eligible for medical, dental, vision, and 401(k).
Work Authorization: Must be authorized to work in the United States. This position is not eligible for sponsorship.
Job Description Addison Group is partnering with a high-growth boutique tactical asset management firm headquartered in Bethesda, MD that is investing heavily in modern data and AI capabilities to sharpen their competitive edge. They are looking to bring on a ML Data Engineer to play a critical role in building and scaling their data infrastructure from the ground up.
This is an opportunity for someone who thrives in a fast-paced startup environment, enjoys owning their work end to end, and wants real upside growth potential in a firm that is building something differentiated in the financial services space. If you are the type of engineer who wants to build, not maintain this is your role
Key Responsibilities
- Design, build, and maintain scalable data pipelines to ingest data from multiple internal and external sources (APIs, SaaS platforms, databases, files).
- Develop and manage a centralized data lake / lakehouse to standardize and curate data for analytics, reporting, and machine learning use cases.
- Implement ELT/ETL processes to clean, validate, transform, and model data into trusted datasets.
- Build and maintain machine-learning-ready datasets and feature pipelines that support experimentation and production models.
- Ensure data quality, freshness, and reliability through monitoring, alerting, and automated validation checks.
- Partner with analytics and business teams to define data requirements, metrics, and reporting outputs.
- Support downstream data consumption for BI tools, dashboards, operational reporting, and partner data exports.
- Apply best practices around data governance, security, access controls, and documentation.
- Collaborate cross-functionally to deliver scalable, maintainable data solutions aligned with business priorities.
- Continuously improve performance, cost efficiency, and reliability of the data platform.
- Bachelor's degree in Computer Science, Data Engineering, Engineering, or a related field (or equivalent experience).
- 4+ years of experience in data engineering or related roles.
- Strong proficiency in Python and SQL.
- Hands-on experience building and operating data pipelines and workflows.
- Experience with modern data platforms (data lakes, data warehouses, or lakehouse architectures).
- Familiarity with orchestration tools (e.g., Airflow, Dagster, Prefect) and data transformation frameworks.
- Solid understanding of data modeling, schema design, and data quality best practices.
- Experience integrating data from APIs and third-party systems.
- Strong problem-solving skills and ability to work independently in a fast-paced environment.
- Excellent communication skills and ability to work with both technical and non-technical stakeholders.
- Experience supporting machine learning workflows (feature engineering, training datasets, or ML pipelines).
- Familiarity with cloud platforms (AWS, Azure, or GCP).
- Experience with streaming or near-real-time data pipelines.
- Knowledge of data governance, security, and compliance best practices.
- Prior experience in financial services, fintech, or regulated data environments.
- Experience working in a high-growth or startup environment.
Vacancy posted 4 hours ago
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