Lead Data Engineer

Ness•Boston, MA

53d•Onsite

About The Position

We are seeking an experienced Lead Data Engineer / Data Architect to spearhead the design, development, and implementation of our enterprise data platform. This role will be instrumental in shaping and driving our data engineering roadmap, ensuring scalability, performance, and innovation. The ideal candidate combines deep technical expertise with strong leadership skills and a solid understanding of financial services data domains.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field.
8+ years of experience in data engineering with a strong focus on Data Architecture, Data Modelling (Conceptual, Logical, Physical), ELT processes and data pipeline development.
Hands-on experience with Snowflake cloud data platform, including data sharing, secure views, and performance optimization.
Proficiency in SQL and familiarity with data integration and ETL/ELT tools.
AWS Technologies (Glue, EMR)
Python for data engineering workflows
Strong understanding of data engineering design patterns
Strong problem-solving skills and the ability to work independently to meet deadlines.
Excellent communication skills for effectively interacting with technical and non-technical stakeholders.

Nice To Haves

Certifications in Snowflake or relevant data technologies.
Experience in the financial services sector, especially asset management / alternative investments, with an understanding of data security and compliance requirements.
Familiarity with cloud platforms (e.g., AWS, Azure) and data orchestration tools (e.g., Apache Airflow).
Knowledge of data visualization tools (e.g., Tableau, Power BI).

Responsibilities

Lead the data architecture and engineering strategy, ensuring alignment with business and technology roadmaps.
Design and implement data models, pipelines, and data integration frameworks across multiple platforms.
Partner with stakeholders to translate business requirements into scalable data solutions.
Performance Optimization: Optimize data pipelines for performance, scalability, and reliability, including query tuning and resource management within Snowflake.
Drive adoption of best practices in data engineering design patterns and modern cloud architectures.
Data Quality Assurance: Implement and monitor data validation procedures to ensure data accuracy and consistency across systems.
Collaboration and Communication: Work closely with project managers, data architects, and business analysts to align project milestones and deliverables with business goals.
Mentor and guide data engineering teams (onsite & offshore) to deliver high-quality outcomes.
Ensure compliance with data governance, security, and privacy standards.
Documentation: Create and maintain detailed documentation of data pipelines, data flow diagrams, and transformation logic.
Issue Resolution: Troubleshoot and resolve issues related to data pipelines, including job failures and performance bottlenecks.