Abinitio Developer

Job Title:

Ab-initio Developer/Data Engineer

Location:

Charlotte, NC (5 Days Onsite) Duration: 24 Months (Possible to convert to Full Time) `

NOTE:

THIS IS ON W2 Job Overview:
Data Engineering aligned with Home lending line of business Designing Blueprints Data modeling and analysis exp required Data transformation and distribution Interact with complex system Should have sourced data from large complex systems ETL/ETL experience for few years Financial Services exp preferred or can consider large Enterprise Clients Strong Python programming experience Key

Skills:

Abinitio, Python, PL/SQL, Teradata, Big Query(preferred), CI/CD, Orchestration, Airflow, Unix Shell Scripting exp Join the Home Lending Data and Insights team as a Software Engineer 3 - Contingent, where candidates will build, run, and modernize enterprise data pipelines in a hybrid landscape—spanning on?prem platforms (Python, Oracle, Teradata and Ab Initio) and a modern Google Cloud stack (Big Query, Dataplex). Candidates will focus on dependable batch and near real-time processing, operational excellence, and the transition from legacy scheduling and ETL patterns to cloud-native orchestration with Google Cloud Composer, backed by strong data quality standards using Informatica Data Quality.

In this role, candidates will:
Build and maintain scalable batch and near real-time data pipelines using AB Initio, Python, PySpark, PL / SQL and SQL to ingest, transform, and publish curated datasets across on-prem and Google Cloud platforms. Develop and optimize Big Query transformations and data models, including partitioning, clustering, query optimization, and cost/performance tuning. Support modernization/migration from Teradata and Ab Initio workflows to GCP/Big Query, including logic re-platforming, reconciliation, parallel runs, and controlled cutovers.

Implement orchestration and scheduling for pipelines using legacy Autosys while driving migration toward Google Cloud Composer (Airflow), including dependency management, retries, SLAs, and backfills. Apply data governance and discovery practices using Dataplex: metadata management, dataset organization, classification support, and ensuring data is consumption-ready. Build and operationalize data quality controls using Informatica Data Quality: profiling, rule implementation, thresholds, exception handling, and embedding quality gates into pipelines.

Ensure operational excellence: monitoring, alerting, runbooks, incident triage/root cause analysis, and continuous improvements to reliability and performance. Implement secure data engineering practices: least-privilege access, PII handling/masking where required, retention controls, and audit-friendly documentation. Partner with product, analytics, and engineering stakeholders to translate requirements into clear data contracts, curated datasets, and maintainable documentation (data dictionaries, reconciliation notes, operational runbooks). Must-have:
Use AI-assisted coding tools (e.g., Git Hub Copilot, Devin, or similar) to accelerate development while maintaining strong code review discipline, testing, and secure coding standards. Closely partner with Product Owners, Architects and Engineers on definition, design, development, integration, testing and support of reliable and reusable Data pipelines. Analyze highly complex business requirements; generate technical specifications to design ETL processes. Act as an expert technical resource for analysis and provides critical direction to less experienced staff.

Work with team members to provide insight into solving complex problems with middleware while leveraging enterprise and industry best practices (including scalability, availability, maintainability, and flexibility).

Required…