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Data Engineer

Argyllinfotech

NULL, NJ

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JOB DETAILS
SKILLS
Agile Programming Methodologies, Amazon Web Services (AWS), Analysis Skills, Application Programming Interface (API), Architectural Analysis, Automation, Banking Services, Business Analysis, Cataloguing, Cloud Computing, Communication Skills, Computer Science, Consulting, Continuous Deployment/Delivery, Continuous Integration, Cost Control, Cryptography, Data Lake, Data Management, Data Modeling, Data Processing, Data Quality, Data Warehousing, Database Extract Transform and Load (ETL), DevOps, Documentation, Ecosystems, Enterprise Data Integration, Enterprise Protection, Financial Regulations, Financial Services, GCP (Good Clinical Practices), Identify Issues, Maintain Compliance, Master Data Management (MDM), Metadata, Microservices, Microsoft Windows Azure, Operational Audit, Operational Support, Performance Tuning/Optimization, Problem Solving Skills, Python Programming/Scripting Language, Regulatory Compliance, Regulatory Reports, Risk, SQL (Structured Query Language), Scalable System Development, Scripting (Scripting Languages), Software Engineering, Source Code/Configuration Management (SCM), Streaming Technology, Structured Data, Systems Engineering, Team Player, Unstructured Data
LOCATION
NULL, NJ
POSTED
4 days ago
Data Engineer
Location: Jersey City, NJ
Position Type: Contract to Hire
Duration: 08/03/2026 08/13/2027
Job Summary
We are seeking a highly skilled Data Engineer with strong experience 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 enterprise-scale data integration initiatives supporting operational, analytical, and regulatory reporting needs.
This role requires hands-on experience in developing modern data pipelines, cloud-native processing frameworks, Master Data Management (MDM), and distributed data ecosystems. The candidate will collaborate closely with data architects, governance teams, analytics organizations, and business stakeholders to deliver secure, scalable, and high-performing enterprise data solutions.
Key Responsibilities
  • Design, develop, and maintain scalable enterprise data pipelines and data integration frameworks.
  • Build and support batch and real-time data ingestion, transformation, and processing solutions.
  • 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 client, account, security, and reference data domains.
  • Collaborate with data architects, business analysts, governance teams, and application teams to support enterprise data initiatives.
  • Implement data quality validation, metadata management, monitoring, and data lineage processes.
  • Participate in cloud migration and modernization initiatives involving legacy and enterprise data platforms.
  • Optimize data processing, pipeline performance, scalability, and storage efficiency across enterprise environments.
  • Ensure compliance with enterprise governance, security, and regulatory standards within financial services environments.
  • Support analytics, reporting, and downstream data consumption platforms through reliable and trusted data delivery solutions.
Required Skills & Experience
  • Strong hands-on experience in Data Engineering and enterprise-scale data integration.
  • Proven experience developing scalable ETL/ELT pipelines and distributed data processing frameworks.
  • Experience working with modern cloud-based data platforms and enterprise data ecosystems.
  • Strong SQL expertise along with programming/scripting experience in Python, PySpark, or Snowpark.
  • Hands-on experience with dbt (Data Build Tool) for:
    • Data transformation and modeling
    • ELT pipeline development within Snowflake and Databricks
    • Modular and reusable SQL-based workflows
    • Data testing, documentation, and version control integration
  • Experience with cloud platforms such as Azure, AWS, or GCP, including integration with Snowflake and Databricks.
  • Solid understanding of data lake, data warehouse, and lakehouse architectures.
  • Experience with orchestration and workflow tools such as Airflow, Databricks Workflows, or Snowflake Tasks.
  • Experience supporting Master Data Management (MDM) and enterprise data governance initiatives.
  • Familiarity with metadata management, data lineage, data cataloging, and data quality frameworks.
  • Experience integrating diverse data sources including:
    • APIs and microservices
    • File-based batch ingestion
    • Real-time and streaming data platforms such as Kafka and Spark Streaming
  • Knowledge of performance tuning, scalability optimization, and cost optimization across Spark and Snowflake environments.
  • Strong understanding of enterprise security, governance, RBAC, data masking, and encryption standards.
  • Experience working in Agile and DevOps environments with CI/CD pipeline implementation for data engineering solutions.
Preferred Qualifications
  • Financial Services or Banking domain experience preferred.
  • Experience supporting regulatory, compliance, risk, or operational reporting environments.
  • Exposure to real-time data processing and streaming technologies.
  • Familiarity with CI/CD automation and infrastructure-as-code practices.
  • Strong analytical, troubleshooting, and problem-solving abilities.
  • Excellent communication and collaboration skills.
Education
Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.

About the Company

A

Argyllinfotech

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