Lead Data Engineer at Great Eastern Life Assurance Co Ltd

Cuenca, Azuay, Ecuador -

Full Time

Start Date

Immediate

Expiry Date

16 Aug, 26

Salary

0.0

Posted On

18 May, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, SQL, Spark, Hadoop, Python, Hive, ETL, Data Modeling, Stakeholder Management, Big Data Ecosystems, Data Governance, Data Pipelines, R, Java, Cloudera, Hortonworks

Industry

Insurance

Description

The Lead Data Engineer will work across IT, Data Management and Governance, Visual Analytics, Data Scientists and key Business users to deliver business analytics contextual datasets and data products that enable data-driven businesses. Architect end-to-end solution for business analytics product (dashboards or statistical model) from the acquiring of data, contextualizing data for business analytics and integrating of product with business process Adopt the best data engineering practices to design and build reliable data marts in the Hadoop ecosystem for planning, reporting, and analytics Work closely with business stakeholders, data scientists and data analysts to communicate data requirements, collect data, and validate quality of data products Collaborate with business stakeholders, data scientists and data analysts to align logics of key metrics, and to ensure code logics correctly reflect latest business definitions Maintain and optimize data pipelines to ensure all data are up to date with data accuracy and integrity Business process owner for onboarding business users and data products onto data platform and the data pipelines that feeds into dashboards or statistical model. Work across various stakeholders to ensure smooth production deployment of data pipeline and adherence to data governance policies. Proactively identify and suggest solutions to improve data engineering process. Takes accountability in considering business and regulatory compliance risks and takes appropriate steps to mitigate the risks. Maintains awareness of industry trends on regulatory compliance, emerging threats and technologies in order to understand the risk and better safeguard the company. Highlights any potential concerns /risks and proactively shares best risk management practices. Desired Experience Bachelor’s Degree in Computer Engineering, Computer Science, Mathematics, Software Engineering, equivalent fields or proven experience in data engineering Stakeholder Management – Conversant in Business terms and ability to resolve and explain data analytics issues with Business users and other concerned stakeholders A minimum 8 years of experience in data engineering field. An analytics practitioner with proven experience in delivering data-driven business solution and data-driven process augmentation The candidate must have a demonstrated experience working with varied forms of data infrastructure inclusive of relational databases such as SQL, Hive, Cloudera, Hadoop, Spark Hands-on experience with large volumes of data using SQL, Spark, Hadoop, or other big data ecosystems is preferred Process oriented, and able to translate complex problems into logical and repeatable processes and diligently document the proposed technical solution Prior experience in creating, managing data models, ETL and data platforms migration is preferred Understanding of banking, insurance and financial services is preferred Technical Skillset Minimum 8 years of working experience in SQL / Hive QL / Spark Minimum 8 years of programming experience in preferred languages Python / R / Java Minimum 8 years of experience in data engineering work on Big Data Technology (Hortonworks / Cloudera) Team Player Able to work with specialist in different disciplines to formulate data solutions Able to work under pressure with or without supervision Able to work collaboratively as part of a team. High level of integrity, takes accountability of work and good attitude over teamwork. Takes initiative to improve current state of things and adaptable to embrace new changes.

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Responsibilities

Architect and deliver end-to-end data products and reliable data marts within the Hadoop ecosystem to enable business analytics. Collaborate with stakeholders and data scientists to align metrics, optimize data pipelines, and ensure adherence to data governance policies.