Sr. Data Engineer with Investment domain experience

NAVA Software is looking for a Sr. Data Engineer with Investment domain experience

Details:

Sr. Data Engineer with Investment domain experience

Location: Holmdel, NJ - Hybrid

Duration: 6+ month

Job Summary

The Senior Data Engineer will design, develop, and support scalable enterprise data solutions for Clients Investments organization. The role will build high-performing data pipelines and data platforms that deliver reliable investment data for reporting, analytics, and downstream business processes.

The engineer will work with investment data across multiple asset classes, including fixed income, equities, commercial mortgage loans, private assets, and derivatives. The role requires strong expertise in modern data engineering, investment data, data quality, and production support, along with the ability to collaborate with product owners, architects, business stakeholders, and engineering teams in an Agile environment.

Key Responsibilities

Requirements Elicitation & Analysis

• Understand product vision, business requirements, data models, and platform objectives.
• Collaborate with Product Owners, SMEs, and Architects to define technical requirements and data mapping specifications.
• Translate business rules into detailed low level technical designs (LLD).

Project Planning, Tracking & Reporting

• Estimate efforts for design, build, and test activities for assigned modules.
• Provide input to sprint planning, schedule creation, and dependency identification.
• Track and report progress, highlighting risks, blockers, and scope variations.
• Ensure timely, high quality delivery of assigned work items.

Design

• Develop low level designs for ingestion, transformation, and delivery pipelines.
• Evaluate alternate solutions and recommend optimal technical approaches.
• Present LLDs in formal reviews and incorporate feedback.

Development & Support

• Build scalable, high performing ETL/ELT pipelines using approved tools and coding standards.
• Implement complex investment data transformations involving trades, positions, valuations, pricing, and security master data.
• Adhere to version control, code management, and DevOps practices.
• Perform peer reviews.

Testing, Debugging & Validation

• Participate in test case walkthroughs and validate coverage for functional and non functional requirements.
• Conduct impact analysis for enhancements and defects.
• Lead debugging efforts for complex data or performance issues; escalate risks as needed.

Documentation

• Create and maintain technical documentation, including data mappings, transformation logic, unit test results, and operational runbooks.
• Ensure documentation meets SDLC and audit requirements.

Process, Compliance & Governance

• Follow established SDLC, data governance, security, and audit frameworks.
• Support internal and external audit requests related to the data platform.
• Ensure engineering practices adhere to corporate standards.

Qualifications & Required Skills

Must Have Skills

Data Engineering and SQL

• Strong experience in SQL development, complex query design, performance tuning, and large-scale data processing.
• Proven experience designing scalable data pipelines and database solutions that support high-volume integration, reporting, and analytics workloads.
• Strong knowledge of relational, dimensional, and analytical data modeling.
• Experience implementing data quality, validation, reconciliation, exception handling, auditability, and restart and recovery capabilities.

Databricks, Spark, and Python

• Strong hands-on experience with Databricks, Apache Spark, PySpark, and distributed data-processing frameworks.
• Experience developing common frameworks for ingestion, transformation, validation, logging, monitoring, and automation.
• Knowledge of Lakehouse architecture and cloud-based data engineering patterns.
• Experience optimizing Spark jobs, partitioning strategies, storage formats, and pipeline performance.
• Strong Python development skills with experience building reusable, modular, testable, and production-ready data engineering solutions.

ETL/ELT and Orchestration

• Experience designing and implementing enterprise-scale ETL/ELT pipelines using Informatica or equivalent data integration platforms
• Experience building workflows with robust scheduling, dependency management, error handling, recovery, and operational monitoring.
• Experience with enterprise scheduling and orchestration tools such as Control-M or comparable platforms.

Investment Data

• Strong understanding of investment data, including securities, trades, transactions, positions, holdings, cash, pricing, valuations and benchmarks.
• Familiarity with multiple asset classes, including fixed income, equities, commercial mortgage loans, private equity, private placements, and derivative products.
• Understanding of security master, reference data, market data, investment accounting, and downstream reporting processes.
• Ability to translate investment business rules into technical mappings, transformation logic, and validation controls.

DevOps and Delivery

• Experience with Git-based version control and CI/CD platforms such as Azure DevOps, Jenkins, or GitHub Actions.
• Experience with automated testing, deployment, release management, and environment configuration.
• Experience working in Agile delivery environments using Scrum, Kanban, or SAFe practices.
• Strong collaboration skills with product owners, architects, business stakeholders, quality assurance teams, and engineers.

Nice to have

• Experience working with investment accounting and investment data management platforms such as Eagle PACE, Neoxam
• Experience designing or integrating generative AI solutions into enterprise data platforms and business workflows.
• Understanding of large language models, prompt engineering, embeddings, semantic search, vector databases, and Retrieval-Augmented Generation architectures.
• Experience using AI-assisted engineering tools such as GitHub Copilot, Microsoft Copilot, or Databricks Assistant to improve development, testing, documentation, or operational productivity.

Education & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Typically 8-10 years of experience in data engineering roles.

Behavioral Competencies

• Analytical and detail oriented with strong problem solving skills.
• Highly collaborative and comfortable working with cross functional teams.
• Effective in communicating technical concepts to business stakeholders.
• Self driven with an ownership mindset and focus on delivery excellence.
• Adaptable and able to thrive in a fast paced, Agile environment.

Additional Information

Occasional evening or weekend work may be required during critical releases or production support windows.