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Senior Consultant, Application

Date: 10-Nov-2020

Location: Singapore, Singapore

Company: Singtel

NCS is looking for a data engineer to operationalize the data integration and management process – to ingest data from numerous data sources and apply transformations for data quality and insights. The data engineer will work closely with the business users, project managers, technical teams like database engineers and source system data owners to develop data pipelines to automate data acquisition and cleansing, to sustain analytics and AI initiatives.

The role requires the ability to translate business and technical requirements into data interfaces, data transformation jobs and design data models that powers self-service analytics or AI projects. To support this, you may also need to establish data management processes such as data governance, data cataloguing, security/privacy classification and advise our clients on the collection, storage and consumption of information in their organisations.


What will you do?

  • Design and implement relevant data models in the form of data marts stored in Operational Data Stores, Data Warehouses or Big Data platforms
  • Build data pipelines to bring information from source systems, harmonise and cleanse data to support analytics initiatives for core business metrics and performance trends.
  • Perform data profiling to understand data quality and advise practical measures to address such data issues through data transformation and data loading
  • Dive into company data to identify sources and features that will drive business objectives.
  • Work closely with project manager and technical leads to provide regular status reporting and support them to refine issues/problem statements and propose/evaluate relevant analytics solutions
  • Bring your experience and ideas to effective and innovative engineering, design and strategy
  • Work in interdisciplinary teams that combine technical, business and data science competencies that deliver work in waterfall or agile software development lifecycle methodologies
  • The range of accountability, responsibility and autonomy will depend on your experience and seniority, including:
    • Contributing to our internal networks and special interest groups
    • Mentoring to upskill peers and juniors


The ideal candidate should possess / be: 

  • Good communications skills to understand our customers' core business objectives and build end-to-end data centric solutions to address them
  • Good critical thinking and problem-solving abilities
  • Prior experience building large scale enterprise data pipelines using commercial and/or open source data management tools from vendors such as Informatica, Talend, Microsoft, IBM or Oracle
  • Strong knowledge of data manipulation languages such as SQL necessary to build and maintain complex queries and data pipelines
  • Practical appreciation of data quality metrics and remediation strategies
  • Data modelling and architecting skills including strong foundation in data warehousing concepts, data normalisation, and dimensional data modelling such as OLAP
  • Undergraduate or graduate degree in Computer science or equivalent
  • Experience with other aspects of data management such as data governance, metadata management, archival, data lifecycle management
  • Processing of semi-structured and unstructured data sets such as NoSQL, graph and Hadoop based data storage technologies such as MongoDB, Cassandra, HBase, Hortonworks/Cloudera, Elastic Search and Neo4j using Spark, Splunk or Apache Nifi for batch or streaming data
  • Large scale data loading experience moving enterprise or operational data from source systems to new applications or data analytics solutions
  • Experience in leveraging on cloud-based data analytics platform such as:
    • AWS serverless architecture in Lambda on AWS DynamoDB, EMR Redshift
    • Azure Data Factory or SQL Data Warehouse
    • GCP BigQuery/BigTable, Cloud Dataprep/Dataflow/Dataproc