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Associate Data Engineer Jobs in Hoboken, NJ (NOW HIRING)

T-Mobile

Principal Data Engineer

New York, NY ยท On-site

$125K - $226K/yr

Job Overview We're looking for a Principal Data Engineer who brings strong technical judgment, a ... Associate (MCSA) (Required) * At least 18 years of age * Legally authorized to work in the United ...

Quinnox Inc

Data Engineer (local to NY)

Manhattan, NY ยท On-site

$126K - $151K/yr

... Engineer Associate or Professional certification ย• Experience with data orchestration tools (Apache Airflow, Databricks Workflows) ย• Strong debugging and problem-solving skills ย• Excellent ...

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All JobsAssociate Data Engineer JobsAssociate Data Engineer Jobs in Hoboken, NJ

Associate Data Engineer information

See Hoboken, NJ salary details

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How much do associate data engineer jobs pay per hour?

As of Sep 2, 2026, the average hourly pay for associate data engineer in Hoboken, NJ is $20.50, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $21.83 per hour, depending on experience, location, and employer.

What does an associate data engineer do?

An Associate Data Engineer is responsible for supporting the development, maintenance, and optimization of data pipelines and databases. They work closely with senior data engineers and other IT professionals to ensure data is accessible, reliable, and efficiently processed for analytics and business use. Typical tasks include writing and testing code for data integration, troubleshooting data issues, and implementing data security best practices. This entry-level position is a foundational role that builds technical skills and experience in data engineering.

What are the key skills and qualifications needed to thrive as an associate data engineer?

To thrive as an Associate Data Engineer, you need a solid understanding of data modeling, SQL, Python, and foundational knowledge of database concepts, often backed by a degree in computer science or a related field. Familiarity with data warehousing tools (like AWS Redshift, Google BigQuery), ETL frameworks, and cloud platforms as well as industry certifications such as AWS Certified Data Analytics is beneficial. Strong problem-solving skills, attention to detail, and effective communication help you navigate complex data challenges and collaborate with teams. These abilities are crucial for ensuring data systems are reliable, scalable, and aligned with organizational goals.

What are some common challenges an associate data engineer may face when working with large-scale data pipelines?

As an Associate Data Engineer, you may often encounter challenges such as optimizing data pipeline performance, ensuring data quality, and troubleshooting bottlenecks when processing large volumes of data. Working with distributed systems can introduce complex issues like latency and data consistency. Collaborating effectively with data scientists, analysts, and senior engineers is crucial for aligning data infrastructure with evolving project requirements. Regularly learning new tools and best practices will help you adapt to these challenges and grow in your role.

What is the difference between Associate Data Engineer vs Data Engineer?

AspectAssociate Data EngineerData Engineer
Required CredentialsBachelor's degree in CS, Data Science, or related field; basic knowledge of SQL and PythonBachelor's or Master's degree; advanced knowledge of SQL, Python, Spark, and cloud platforms
Work EnvironmentEntry-level, team-focused, often in tech or finance industriesMid to senior level, designing and maintaining data pipelines in various industries
Employer & Industry UsageCommon in tech companies, startups, and finance firmsUsed across industries for building scalable data infrastructure
Common Search & ComparisonOften compared for career progression and skill requirements

The Associate Data Engineer role is an entry-level position focusing on supporting data infrastructure, while the Data Engineer is a more advanced role responsible for designing and maintaining complex data systems. The roles share similar educational backgrounds and work environments but differ in experience level and responsibilities.

Is an associate data engineer entry level?

An associate data engineer is typically an entry-level position suitable for candidates with limited professional experience in data engineering. It often requires foundational skills in SQL, Python, or cloud platforms and serves as a starting point for a career in data engineering.

What are the most commonly searched types of Data Engineer jobs in Hoboken, NJ?

The most popular types of Data Engineer jobs in Hoboken, NJ are:

What are popular job titles related to Associate Data Engineer jobs in Hoboken, NJ?

For Associate Data Engineer jobs in Hoboken, NJ, the most frequently searched job titles are:

What job categories do people searching Associate Data Engineer jobs in Hoboken, NJ look for?

The top searched job categories for Associate Data Engineer jobs in Hoboken, NJ are:

What cities near Hoboken, NJ are hiring for Associate Data Engineer jobs?

Cities near Hoboken, NJ with the most Associate Data Engineer job openings:

Associate Data Engineer jobs near you
Infographic showing various Associate Data Engineer job openings in Hoboken, NJ as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $42,641 per year, or $20.5 per hour.

Senior Data Engineer Investment Banking / Risk

Hudson Data LLC

Manhattan, NY โ€ข On-site

$116K - $158K/yr

Other

Posted 13 days ago

Job description

Job Title: Senior Data Engineer Investment Banking / Risk
Location: New York, NY
Type: W2 role
Domain: Capital Markets Investment Banking & Risk

Role Summary

We are seeking a Senior Data Engineer to design, build, and optimize large-scale data pipelines supporting Investment Banking and Risk functions. The ideal candidate combines deep hands-on engineering expertise in the Azure Databricks ecosystem with a strong understanding of financial risk data (market risk, credit risk, counterparty risk, regulatory reporting). This role partners closely with quants, risk analysts, and front-office stakeholders to deliver trusted, performant, and audit-ready data products.

Key Responsibilities

  • Design and build scalable ETL/ELT pipelines on Azure Databricks using PySpark and Spark SQL to ingest, transform, and curate large volumes of trade, position, market, and reference data.
  • Develop and maintain the Medallion (Bronze/Silver/Gold) architecture on Delta Lake, ensuring data quality, lineage, and reconciliation across risk and finance datasets.
  • Translate risk and regulatory requirements (e.g., Basel, FRTB, CCAR, VaR, PFE, stress testing) into robust data models and engineering solutions.
  • Optimize Spark jobs for performance and cost partitioning, caching, broadcast joins, Z-ordering, and cluster tuning.
  • Build and orchestrate workflows using Databricks Workflows / Azure Data Factory, integrating with ADLS Gen2, Azure Key Vault, and CI/CD pipelines.
  • Implement data quality, validation, and controls frameworks appropriate to a regulated financial environment.
  • Collaborate with quants and risk teams to productionize models and analytical datasets.
  • Contribute to code reviews, engineering standards, and documentation.

Required Qualifications

  • 7+ years of data engineering experience, with 3+ years in Investment Banking, Capital Markets, or Risk.
  • Strong domain knowledge of risk data market risk, credit risk, counterparty risk, P&L, or regulatory/reg reporting.
  • Expert-level Python for data engineering (Pandas, PySpark APIs, modular/production-grade code).
  • Advanced PySpark and Spark SQL for distributed data processing at scale.
  • Hands-on Azure Databricks and Delta Lake experience (notebooks, Unity Catalog, clusters, jobs).
  • Strong SQL skills complex queries, window functions, performance tuning.
  • Experience with the broader Azure data stack: ADLS Gen2, Azure Data Factory, Key Vault, Synapse (a plus).
  • Solid understanding of data modeling (dimensional, normalized), data warehousing, and lakehouse patterns.
  • Experience with version control (Git), CI/CD, and Agile delivery.

Preferred / Nice-to-Have

  • Databricks certification (Data Engineer Associate/Professional).
  • Exposure to trade lifecycle, OTC derivatives, fixed income, or equities data.
  • Familiarity with regulatory frameworks (FRTB, Basel III/IV, BCBS 239).
  • Experience with streaming (Structured Streaming, Kafka/Event Hubs).
  • Knowledge of data governance, lineage, and Unity Catalog access controls.

Education

Bachelor's or Master's in Computer Science, Engineering, Finance, or a related quantitative field.

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