In this role you will Build robust batch/streaming data pipelines and transformations that ensure high‑quality, governed data flows into AI and analytics platforms. Implement data modeling, quality checks, lineage/metadata, performance optimization, and reliable orchestration across cloud and Lakehouse environments. The position is located in New York, NY.
In this role, you’ll make an impact in the following ways:
Design, build, and optimize batch and streaming data pipelines that deliver high-quality, governed data to AI, analytics, and enterprise platforms.
Develop scalable transformation frameworks across cloud and Lakehouse environments with a strong focus on reliability, performance, and cost efficiency.
Implement data modeling, schema design, lineage, metadata, and data quality controls to create trusted, reusable data assets.
Build and support orchestration and workflow automation for complex pipelines and dependencies using modern data engineering tools.
Partner with analytics, AI, business, and technology teams to translate requirements into scalable, secure, and governed data solutions.
Resolve complex platform and pipeline issues related to performance, reliability, integration, and data quality.
Promote engineering best practices in distributed processing, testing, monitoring, security, and continuous improvement.
Provide technical leadership and mentoring while contributing to the adoption of modern data engineering and Lakehouse capabilities.
To be successful in this role, we’re seeking the following:
Bachelor’s degree in computer science, Engineering, or a related field, or equivalent experience.
6+ years of experience in software development and data engineering; financial services experience is a plus.
Strong experience building large-scale batch and streaming data pipelines and backend data environments.
Deep expertise in Spark/Databricks, Kafka, SQL, and orchestration tools such as Airflow and/or dbt.
Strong understanding of cloud and lakehouse architectures, distributed processing, and storage formats such as Parquet and Delta.
Experience with data modeling, schema design, lineage, metadata, and data quality frameworks.
Proven ability to improve performance, scalability, reliability, cost efficiency, and governance across data platforms.
Strong stakeholder management, problem-solving, and technical leadership skills, including mentoring engineers and leading complex delivery.
Job description provided by BNY.
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