Teradata Engineer

**Job Information**:
Salary

**7000***

Industry

**Banking***

Date Opened

**10/04/2024***

Job Type

**Full time***

State/Province

**singapore***

City

**Raffles Link***

Zip/Postal Code

**520213***

Country

**Singapore***
- Provide technical vision and create roadmaps to align with the long-term technology strategy
- Proficient in building data ingestion pipelines to ingest data from heterogeneous sources like RDBMS, Hadoop, Flat Files, REST APIs, AWS S3
- Key player in Hadoop Data Ingestion team which enables data science community to develop analytical/predictive models and implementing ingestion pipelines using Hadoop Eco-System Tools Flume, Sqoop, Hive, HDFS, Pyspark, Trino and Presto sql.
- Extensively use DataStage, Teradata\Oracle utility scripts and Data stage jobs to perform data transformation/Loading across multiple FSLDM layers
- Engage users to achieve concurrence on technology provided solution. Conduct review of solution documents along with Functional Business Analys and Business Unit for sign-off
- Create technical documents (functional/non-functional specification, design specification, training manual) for the solutions. Review interface design specifications created by development team
- Participate in selection of product/tools via RFP/POC.
- Provide inputs to help with the detailed estimation of projects and change requests
- Execute continuous service improvement and process improvement plans

**Requirements**:

- Bachelors degree in computer science or similar relevant education background.
- 3 - 7 years of experience with Data Engineering experience in the banking domain including implementation of Data Lake, Data Warehouse, Data Marts, Lake Houses etc
- Experience in data modeling for large scale data warehouses, business marts on Hadoop based databases, Teradata, Oracle etc for a bank
- Expertise in Big Data Ecosystem such as Cloudera (Hive, Impala, Hbase, Ozone, Iceberg), Spark, Presto, Kafka
- Experience in a Metadata tool such as IDMC, Axon, Watson Knowledge Catalog, Collibra etc
- Expertise in operationalizing machine learning models including optimizing feature pipelines and deployment using batch/API, model monitoring, implementation of feedback loops
- Knowledge of report/dashboards using a reporting tool such as Qliksense, PowerBI