Data Engineer / Ab Initio Migration / Contract-to-Hire / Onsite / Charlotte, NC

Motion Recruitment; Charlotte (NC); On-site; USD 110,000 - 150,000; Full time

Job summary

Motion Recruitment represents a leading global banking client hiring an on-site Data Engineer in Charlotte, NC. You will build and modernize enterprise data pipelines across on-prem and Google Cloud, focusing on reliability, governance, and performance.

You’ll migrate legacy ETL patterns to Cloud Composer (Airflow) and implement robust data quality controls with Informatica Data Quality, while partnering with product and analytics teams.

Qualifications

• 4+ years in data engineering with relevant tech stack.
• Proficiency in SQL, PL/SQL on Oracle/Teradata.
• Experience with on-prem and Google Cloud data platforms.

Responsibilities

• Build and maintain scalable batch and near real-time pipelines using AB Initio, Python, PySpark, PL/SQL, SQL.
• Develop and optimize BigQuery transformations and data models.
• Support modernization from Teradata and Ab Initio to GCP/BigQuery with cutovers.
• Implement orchestration using Autosys and migrate to Cloud Composer/Airflow.
• Apply data governance with Dataplex for metadata management and data availability.
• Build data quality controls with Informatica Data Quality.
• Ensure monitoring, incident triage, and continuous improvements.
• Maintain secure data practices and documentation.

Skills

SQL

Python

PySpark

BigQuery

Oracle

Teradata

Ab Initio

ETL design

Data pipelines

Data governance

Tools

Autosys

Airflow

Cloud Composer

Dataplex

Informatica Data Quality

GitHub Copilot

Job description

Our client, one of the leading global banking companies, is hiring for a Data Engineer. This is an onsite position in Charlotte, NC.

This is on their Home Lending Data team where you’ll 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 (BigQuery, Dataplex).

You’ll 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.

Duration: 24 Month Contract to Start with Intent to Convert to FTE

In this role, you 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 BigQuery transformations and data models, including partitioning, clustering, query optimization, and cost/performance tuning.
• Support modernization/migration from Teradata and Ab Initio workflows to GCP/BigQuery, 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., GitHub 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 Qualifications:

• 4+ years of Data Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 4+ years PL/SQL and SQL skills with proven experience in Oracle, Teradata, Python and/or BigQuery: complex query development, tuning, and debugging.
• 4+ years Ab Initio skills with proven experience to build complex graphs, Psets, performance tuning.
• 3+ years programming skills in Python; hands-on PySpark for distributed data processing
• 3+ years of ETL/ETL design, data warehousing concepts, and data modeling best practices

Desired Qualifications:

• Production operations experience: monitoring, SLAs, incident response, root cause analysis, and performance optimization.
• Experience working in hybrid environments (on-prem + cloud) and supporting data migration/modernization initiatives.
• Experience with scheduling/orchestration in Autosys and Airflow-based orchestration (Cloud Composer direction).
• Experience with Git-based workflows, code reviews, and automated testing practices for data pipelines.
• Experience with Harness, Jenkins and uDeploy based CICD environments.
• Practical experience using AI-assisted coding tools in daily development to improve productivity without compromising quality or security.
• Ab Initio development/maintenance experience and/or hands-on migration of Ab Initio graphs to modern Spark/SQL patterns.
• Experience with Dataplex and broader data governance concepts (metadata, classification, stewardship, lineage practices).
• Experience with Informatica Data Quality implementation patterns (profiling, rules, scorecards/metrics, exception workflows).
• Experience designing near real-time patterns (micro-batch/event-driven concepts) and handling late-arriving/out-of-order data.
• Familiarity with GCP operational practices for data workloads (service accounts/IAM basics, job monitoring, quota/cost controls).