Hybrid Staff Data Engineer, Data & AI presso Dealmaker
Dealmaker · New York, Stati Uniti d'America · Hybrid
- Ufficio in New York
DealMaker is a fast-growing fintech company revolutionizing the capital markets ecosystem with a mission to make online capital raising mainstream. We empower founders, CEOs, and operators to raise capital digitally, both from their own communities and through strategically marketed campaigns. No other platform provides an end-to-end solution like ours—and our track record speaks for itself, with over $2B raised across 1,000+ campaigns. We power the largest online capital raises for customers like EnergyX ($88M), Green Bay Packers ($65M), Miso Robotics ($72M+), Monogram Orthopaedics (Nasdaq:MGRM) and many others, with 3 IPOs in the past year alone. We are quickly expanding our horizons and are seeking talented team members to join us on our journey to transform the global capital market.
Who you are
You are a pragmatic problem-solver with a strong bias for action, aligning perfectly with our "Prioritize Speed over Perfection" value. You're passionate about data's power to "Drive Outcomes" and are constantly seeking to "Push Your Limits" by learning and applying new technologies. When faced with complex data challenges, you "Find a Way" to deliver robust and innovative solutions. You're "Bold and Direct" in your technical recommendations and committed to "Innovating and Simplifying" our data landscape. Above all, you "Obsess over Customers," recognizing that reliable and accurate data is paramount to their success and ours.
What you will do
As a Staff Data Engineer, you will be instrumental in building the data backbone that powers DealMaker's analytics and AI capabilities. You'll play a pivotal role in enabling our data-driven growth, reporting directly to the VP of Data and AI. This is a critical Senior Individual Contributor role within a dynamic team of four, where your impact will be felt across every analytics and AI initiative at DealMaker. Your primary responsibilities will include:
- Data Pipeline Development & Optimization: Design, build, and maintain robust, scalable, and efficient ETL/ELT pipelines to ingest, transform, and load data from various internal and external sources into our data warehouse/data lake. You will be critical in ensuring high data pipeline uptime (99% target) and reliability to support all analytics and AI initiatives.
- Data Architecture & Modeling: Collaborate with stakeholders across product, engineering, and business teams to define and implement optimal data models and schemas that support both analytical reporting and sophisticated machine learning applications.
- Data Quality & Governance: Implement and enforce stringent data quality checks, data validation rules, and data governance best practices to ensure the integrity, accuracy, and reliability of our data assets. Your work will directly contribute to maintaining high standards for data accuracy across key business metrics (+/- 5% target for Sales, Revenue, Shareholder Services, and OPSP).
- MLOps Implementation: Transition machine learning models from development to production, implementing MLOps practices such as model versioning, deployment, monitoring, and retraining.
- ML Model Development: Take an active part in the design, development, and validation of machine learning models, leveraging engineering best practices for code quality, scalability, and maintainability.
- Automation for AI Initiatives: Develop automated data pipelines and processes to support and accelerate our AI initiatives, including automating deal forecasting updates (targeting 20% of deals) and achieving high forecast accuracy (<10% revenue variance).
- Performance Tuning & Infrastructure: Proactively identify and resolve performance bottlenecks in data pipelines and queries, optimizing our data infrastructure for scalability and efficiency.
- Collaboration & Mentorship: Provide technical guidance and mentorship to other team members, fostering a culture of excellence and continuous improvement.
- Technology Evaluation & Adoption: Stay abreast of emerging data technologies, tools, and best practices in data engineering, data science, and MLOps, and recommend their adoption where beneficial.
- Documentation: Create and maintain comprehensive documentation for data pipelines, data models, and data governance procedures.
What skills you need
- Demonstrated years of experience in a Data Engineering role, with a proven track record of building and maintaining production-grade data pipelines.
- Strong proficiency in SQL, Python, and experience with relational and/or NoSQL databases (e.g., PostgreSQL, Snowflake, Databricks, etc.).
- Hands-on experience with cloud platforms such as AWS, Azure, or GCP, and their respective data services (e.g., AWS S3, Glue, Redshift, Azure Data Factory, Google Cloud Dataflow/BigQuery), with a preference given to AWS experience.
- Solid understanding of data warehousing concepts, data modeling (dimensional modeling, etc.), and ETL/ELT processes.
- Working knowledge of machine learning fundamentals and experience collaborating with data scientists on model deployment and integration into production environments.
- Experience with MLOps concepts and tools for model lifecycle management (e.g., MLflow, CI/CD pipelines for ML models).
- Excellent problem-solving skills and the ability to troubleshoot complex data issues.
- Strong communication and collaboration skills, with the ability to translate technical concepts for non-technical audiences.
- A proactive, self-starter attitude with a desire to learn and grow in a fast-paced environment.
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.
Bonus Points
- Experience with real-time data streaming technologies (e.g., Apache Kafka, Kinesis).
- Prior experience in the FinTech or financial services industry.
- Contributions to open-source projects in data engineering or MLOps.
