Data Platform Engineer

Versana•New York, NY

7d•$130,000 - $160,000

About The Position

Versana is an industry-backed data and technology company on a mission to transform the syndicated loan market. By digitally capturing agent banksâ data on a real-time basis, Versana provides unprecedented transparency into loan-level details and portfolio positions, bringing efficiency and velocity to the entire market. Through our platform, participants can rest assured they are accessing the loan marketâs most credible source of deal information. Versana is looking for a motivated mid-level Data Platform Engineer to join our team. This role is primarily focused on data engineering, with a secondary responsibility in reporting and dashboard delivery. Your mission is to transform operational (OLTP) data from multiple source systems into clean, analysis-ready (OLAP) datasets using our lakehouse medallion architecture. You will work closely with seasoned technology leaders, and colleagues with diverse experience in a dynamic, agile environment. Youâll mentor team members through code reviews, pairing, documentation, and knowledge-sharing practices. Youâll both share your engineering expertise with colleagues and learn from their deep domain knowledge. Youâll help standardize data transformations and testing, and drive CI/CD practices, all while improving performance, reliability, and observability.

Requirements

B.S. or B.A in Computer Science or related field.
5â7 years in data engineering or analytics engineering.
Strong SQL and practical experience with dbt for transformations and testing.
Hands-on experience with data modeling and performance optimization.
Professional coding experience with both Java and Python.
Experience operating data ware/lake-houses and building semantic layers.
Familiarity with CI/CD (DataOps), version control, and environment promotion.
Data quality, observability, and alerting experience with a focus on SLAs and stakeholder trust.
Experience with external client reporting, embedded analytics, and multi-tenant considerations (modeling, partitioning, access controls).
Demonstrated ability to mentor or coach in software engineering practices.
Effective communication and requirement gathering

Nice To Haves

Cost/performance tuning across data ware/lake-housing, query engines, BI tools; caching strategies and aggregation design.
Data documentation, lineage, and cataloging practices; strong habits around reproducibility and testing.
Apply report design and visualization best practices (effective layouts, clear metric definitions, consistent interactions, and performant SQL+DAX queries) to deliver executiveready dashboards.
Experience with any of the following: Azure Fabric, Power BI, Dremio, Apache Iceberg, Apache Parquet, Datadog

Responsibilities

Design, implement, and operate ELT pipelines to ingest data into the lakehouse.
Apply Medallion architecture and semantic layering to deliver curated datasets.
Build and maintain durable data lakes sourced from operational systems, including sustainable ingestion, schema evolution, storage formats, partitioning, and performance.
Secure data at rest and in transit by enforcing governance policies, including regular access reviews and auditability.
Establish and enforce standards for data quality, observability/alerting, and lineage; uphold daily/hourly SLAs for critical reports and datasets.
Drive CI/CD for data code reviews, documentation, and environment promotion; mentor and unblock teammates.
Create and maintain tabular models and dataflows; optimize dataset refreshes, query performance, and usability.
Partner with Product to translate requirements into robust models, metrics, and dashboards; collaborate with Application Developers on data contracts and change management for upstream systems.
Support operational monitoring and alerting; ensure timely, trusted reporting for leadership and clients.
Triage and fulfill ad-hoc data requests while improving self-service patterns and documentation.