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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Yoh Services LLC - **Location:** Jersey City, NJ, United States - **Salary:** $135,200.0 - $166,400.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Airflow, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Cloud Computing, Cloud Database, Cloud Engineering, Information Systems, Computer Programming, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Masking, Data Transformation, Dataspaces, Data Warehousing, Database Testing, DevOps, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Meta-Data Management, Oracle (Applications), Performance Tuning, Role-Based Access Control, Reference Data, Cloud Services, Standard Sql, SQL Databases, Data Streaming, Workflow Management Systems, Enterprise Data Management, Data Processing, Google Cloud, Cloud Platform System, Snowflake, Data Build Tool (dbt), Apache Spark, Data Lakes, Pyspark, Infrastructure Automation Frameworks, Information Technology, Data Lineage, Real Time Data, Apache Kafka, Spark Streaming, Data Management, Video Streaming, Cloud Migration, Data Delivery, Stream Processing, Software Version Control, Data Pipelines, Databricks, Microservices - **Published:** August 31, 2026 - **Apply:** https://www.dice.com/job-detail/76b5272f-babb-4eb9-af7e-5f00364e1a25 ## About the Role * Financial services experience * Data pipeline development * Databricks/Delta Lake * Master Data Management (MDM)(Golden Source) integration * Strong SQL and data engineering skills * Oracle to Cloud migration experience * Data quality and reconciliation, Strong hands-on experience in Data Engineering and enterprise-scale data integration. Proven experience developing scalable ETL/ELT pipelines and distributed data processing solutions. Experience working with modern cloud-based data platforms and data ecosystems. Hands-on expertise with: Strong SQL expertise along with programming/scripting experience in Python, PySpark, or Snowpark. Experience with dbt (Data Build Tool) for: o Data transformation and modeling o ELT pipeline development within Snowflake/Databricks o Modular, reusable SQL-based data workflows o Data testing, documentation, and version control integration Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform, including integration with Snowflake and Databricks. Solid understanding of data lake, data warehouse, and lakehouse architectures, and their implementation across platforms. Experience with orchestration and workflow tools (e.g., Airflow, Databricks Workflows, Snowflake Tasks) for pipeline scheduling and automation. Experience supporting Master Data Management (MDM) and enterprise data governance initiatives. Familiarity with metadata management, data lineage, data cataloging, and data quality processes. Experience integrating diverse data sources, including: o APIs and microservices o File-based ingestion (batch) o Real-time/streaming data (e.g., Kafka, Spark Streaming) Knowledge of performance tuning, cost optimization, and scalability techniques across both Spark-based and Snowflake environments. Understanding of enterprise security, compliance, and governance standards, including RBAC, data masking, and encryption. Experience working in Agile and DevOps environments, including CI/CD for data pipelines., Financial Services or Banking industry experience preferred. Experience supporting regulatory, risk, compliance, or operational reporting data environments. Exposure to real-time data processing and streaming technologies. Familiarity with CI/CD processes and infrastructure automation. Strong analytical, troubleshooting, and problem-solving skills. Excellent communication and collaboration skills. Education: Bachelor s degree in Computer Science, Information Systems, or Engineering ## Description We are seeking a hands-on Data Engineer with strong experience in building scalable enterprise data solutions within Financial Services environments. The ideal candidate will have expertise in cloud-based data platforms, modern data engineering practices, and large-scale data integration initiatives supporting operational, analytical, and regulatory data needs. This role requires strong technical capabilities in data pipeline development, cloud data processing, Master Data Management (MDM), and enterprise data integration. The candidate should be comfortable working across complex distributed environments and partnering with architecture, analytics, governance, and business teams to deliver reliable, secure, and scalable data solutions., Design, develop, and support scalable data pipelines and enterprise data integration solutions. Build and maintain batch and real-time data ingestion, transformation, and processing frameworks. Develop cloud-native data engineering solutions supporting enterprise data lake, warehouse, and lakehouse platforms. Implement ETL/ELT processes for structured, semi-structured, and unstructured data sources. Support Master Data Management (MDM) initiatives across security, account, client, and reference data domains. Collaborate with data architects, business analysts, governance teams, and application teams to support enterprise data initiatives. Implement data quality validation, monitoring, metadata management, and lineage processes. Support cloud migration and modernization efforts involving legacy and enterprise data platforms. Optimize data processing, storage, and pipeline performance for scalability and operational efficiency. Ensure compliance with enterprise security, governance, and regulatory standards within financial services environments. Support reporting, analytics, and downstream consumption platforms through reliable and trusted data delivery. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)