ETL Data Engineer - HYBRID

NTT DATA Services•Halifax, NS

9h•Hybrid

About The Position

NTT DATA is seeking an ETL Data Engineer for a HYBRID position in Halifax, Nova Scotia, Canada. The role involves designing, developing, and maintaining enterprise cloud data warehouse solutions, building scalable data ingestion and transformation pipelines, and enabling enterprise reporting and analytics. The engineer will work with various source systems, implement data modeling techniques, and leverage cloud-native platforms. Responsibilities also include ensuring data quality, governance, security, optimizing performance, and automating processes through DevOps practices.

Requirements

6+ years of overall experience in data engineering, data warehousing, or cloud data platform development.
6+ Years of Strong hands-on experience in: SQL (advanced), Python / Spark / Scala, ETL/ELT frameworks, Data modeling and warehousing concepts.
6+ Years of Experience with at least one major cloud data platform: Snowflake, Databricks.
6+ Years of Experience building enterprise-scale data pipelines and ingestion frameworks.
6+ Years of Languages & Processing: SQL, Python, PySpark, Scala
6+ Years of Cloud & Warehousing: AWS, Azure, Snowflake, Databricks, Redshift, Synapse
6+ Years of Data Engineering Tools: Airflow, dbt, Kafka, Informatica, Talend
6+ Years of DevOps & Automation: Git, CI/CD, Terraform, Docker, Kubernetes
6+ Years of Data Governance: Lineage, Metadata, Data Quality, Data Reconciliation

Responsibilities

Design, develop, and maintain enterprise cloud data warehouse solutions supporting business, risk, finance, and regulatory reporting needs.
Build scalable and resilient data ingestion, transformation, and orchestration pipelines using cloud-native technologies.
Develop and optimize ETL/ELT frameworks for structured, semi-structured, and streaming datasets.
Enable enterprise reporting and analytics by delivering curated, governed, and trusted datasets.
Design and implement batch and near real-time data pipelines.
Build reusable ingestion frameworks for multiple source systems including: Trading platforms, Risk systems, Treasury and finance applications, Core banking systems, Market and reference data platforms.
Implement metadata-driven and configurable pipeline architectures.
Design and implement dimensional models, star schemas, snowflake schemas, and curated data marts.
Support enterprise data warehouse optimization, partitioning, clustering, and performance tuning.
Build semantic and consumption-ready datasets for downstream analytics and reporting.
Develop solutions leveraging cloud-native platforms such as: AWS / Azure / GCP, Snowflake / Databricks, Cloud storage and compute services.
Implement scalable processing frameworks and distributed compute patterns.
Implement data quality checks, reconciliation controls, lineage, and observability frameworks.
Ensure compliance with banking regulatory, security, and governance requirements.
Support enterprise metadata management and lineage tracking.
Apply secure access models and data masking standards for sensitive financial data.
Optimize warehouse performance, compute utilization, and storage costs.
Improve query performance, data refresh SLAs, and pipeline reliability.
Implement partitioning, indexing, clustering, and workload optimization techniques.
Build CI/CD pipelines for data engineering deployments.
Automate testing, deployment, monitoring, and operational support processes.