Interested in this Data Engineer role at BNY?
Apply Now →About This Role
At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting\-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide.
Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what \#LifeAtBNY is all about. Join us and be part of something extraordinary.
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 Jersey City, NJ.
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.
- 9–11 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.
*
At BNY, our culture speaks for itself, check out the latest BNY news at:
BNY Newsroom
BNY LinkedIn
Here’s a few of our recent awards:
America’s Most Innovative Companies, Fortune, 2025
World’s Most Admired Companies, Fortune 2025
“Most Just Companies”, Just Capital and CNBC, 2025
Our Benefits and Rewards:
BNY offers highly competitive compensation, benefits, and wellbeing programs rooted in a strong culture of excellence and our pay\-for\-performance philosophy. We provide access to flexible global resources and tools for your life’s journey. Focus on your health, foster your personal resilience, and reach your financial goals as a valued member of our team, along with generous paid leaves, including paid volunteer time, that can support you and your family through moments that matter.
BNY is an Equal Employment Opportunity/Affirmative Action Employer \- Underrepresented racial and ethnic groups/Females/Individuals with Disabilities/Protected Veterans.
BNY assesses market data to ensure a competitive compensation package for our employees. The base salary for this position is expected to be between $126,000 and $210,000 per year at the commencement of employment. However, base salary if hired will be determined on an individualized basis, including as to experience and market location, and is only part of the BNY total compensation package, which, depending on the position, may also include commission earnings, discretionary bonuses, short and long\-term incentive packages, and Company\-sponsored benefit programs.
This position is at\-will and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation) at any time, including for reasons related to individual performance, change in geographic location, Company or individual department/team performance, and market factors.
Salary Context
This $126K-$210K range is above the median for Data Engineer roles in our dataset (median: $168K across 41 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,824 AI roles we're tracking, Data Engineer positions make up 2% of the market. At BNY, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills in Demand for This Role
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $208,300 based on 254 positions with disclosed compensation. This role's midpoint ($168K) sits 19% below the category median. Disclosed range: $126K to $210K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
BNY AI Hiring
BNY has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Product Manager, AI Software Engineer. Positions span Boston, MA, US, Jersey City, NJ, US, New York, NY, US. Compensation range: $210K - $250K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.