Job Description

Responsobilities

Manage multiple data engineering delivery / project teams comprise of internal data engineers and IT service providers to ensure that projects or enhancements are delivered within the agreed scope, budget, and schedule
Work with business stakeholders to develop and analyze big data needs
Design, develop and automate large scale, high-performance distributed data processing systems (batch and/or real-time streaming) that meet both functional and non-functional requirements
Design data models for optimal storages across data layers, workload and presentation retrieval to meet critical business requirements and platform operational efficiency
Deliver high level & detailed design to ensure that the solution meet business requirementsand align to the data architecture principles and technology stacks
Practice high quality data engineering/software engineering towards building data platform infrastructure and data pipelines at scale to deliver Big Data Analytics and Data-Science initiatives
Partner with business domain experts, data scientists, and solution designers to identify relevant data-assets, domain data model and data solutions. Collaborate with product data engineers to coordinate backlog feature development of data pipelines patterns and capabilities
Own and lead data engineering projects; data pipelines delivery with reliable, efficient, testable, & maintainable artifacts, involves ingest & process data from a large number and variety of data sources
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing data products for greater scalability
Drive Cloud data engineering practices and Cloud Lake-house re-platform drive to build & scale Modern Data Platform & Infrastructure
Build, optimize and contribute to shared Data Engineering Frameworks and tooling, Data
Products & standards to improve the productivity and quality of output for Data Engineers
Design and build scalable Data APIs to host Operational data and Data-Lake assert in Data Mesh / Data Fabric Architecture
Drive Modern Data Platform operations using Data Ops, ensure data quality, monitoring the data system. Also support Data science MLOps platform
Drive and deliver industry standard Devops (CI/CD) best practices, automate development and release management
Understand data security standards, use data security guidelines & tools to apply and adhere to the required data controls across data platform, data pipelines, applications and access end points
Support and contribute to data engineering product and data pipeline documentations, development guidelines & standards for data-pipeline, data model and layer design

Requirements

Bachelor’s degree in IT, Computer Science, Software Engineering, Business Analytics or equivalent Work Experience
Minimum of 10 years of experience in Data Engineering, Data Lake Infrastructure, Data Warehousing, Data Analytics tools or related, in design and developing of end-to[1]end scalable data pipelines and data products
Experience in building and operating large and robust distributed data lakes (multiple PBs) and deploying high performance with reliable system with monitoring and logging practices
Experience in designing and building data products and pipelines using some of the most scalable and resilient open-source big data technologies; Spark, Delta[1]Lake, Kafka, Flink, Airflow, Presto and related distributed data processing
Experience with data modelling for data warehousing
Experience with data profiling and data quality tools like Apache Griffin, Deequ, and Great Expectations
Build and deploy high performance modern data engineering & automation frameworks using programming languages such as Scala/Python and automate the big data workflows such as ingestion, aggregation, ETL processing etc
Good understanding of data modeling and high-end design, data engineering / software engineering best practices - include handling and logging errors, monitoring the system, fault tolerant pipelines, data quality and ensuring a deterministic pipeline with DataOps
Experience working in Telco Data Warehouse and / or Data Lake
Excellent experience in using ANSI SQL for relational databases like Postgres, MySql, Oracle and knowledge of Advanced SQL on distributed analytics engines – Databricks SQL, Snowflake, etc
Proficiency programming languages like Scala, Python, Java, Go, Rust or scripting languages like Bash
Experience on cloud systems like AWS, Azure, or Google Cloud Platform o Cloud data engineering experience in at least one cloud (Azure, AWS, GCP)
Experience with Databrick (Cloud Data Lakehouse)
Experience on Hadoop stack: HDFS, Yarn, Hive, HBase, Cloudera, Hortonworks

We are committed to a safe and healthy environment for our employees & customers and will require all prospective employees to be fully vaccinated.

Lead Data Engineer

Singtel

Job Summary

Job Description