Senior Data Engineer – Enterprise Data Platforms

Job Description

We are seeking an experienced Senior Data Engineer to design, build, and operate enterprise-scale data pipelines and datasets that support advanced analytics, enterprise reporting, and machine learning use cases. You will play a key role in shaping the Enterprise Data Strategy and Common Data Model while working closely with data scientists, analysts, and business stakeholders in an Agile environment.

Key Responsibilities

Design, build, and maintain datasets and data pipelines for analytics and data science use cases

Prepare, troubleshoot, and optimize data pipelines to ensure reliability, performance, and data quality

Co-design and evolve the Enterprise Data Strategy and Common Data Model

Implement and operate core Data Platform processes and services

Develop and maintain data pipelines tailored for Data Scientists and analytics teams

Maintain and govern model JSON schemas , metadata, and data definitions

Identify, analyse, and resolve data quality and data consistency issues

Support :

Enterprise reporting and analytics

Machine Learning operations (MLOps)

Collaborate with stakeholders across IT, data science, analytics, and business domains using Agile delivery methods

Requirements

Proven experience working with Big Data platforms and technologies , including :

S3, Hive, Spark, Trino, MinIO

Kubernetes (K8S) and Kafka

Strong experience with SQL-based and relational data systems , including PL / SQL

Experience handling banking or financial data , including governance and regulatory considerations

Hands-on experience with large-scale on-premises Data Lake migrations

Integration of Data Science workbenches such as :

KNIME, Cloudera, Dataiku (or similar platforms)

Experience working in Agile environments (Scrum, SAFe)

Proven stakeholder management and cross-team collaboration skills

Technical Skills

Strong understanding of enterprise data reference architectures

Expert-level SQL and data modelling skills

Python for : Automation

Data processing

Notebooks and analytics workflows

Data preparation techniques for :

Reporting

Advanced analytics

Machine learning

Experience implementing and working with data quality frameworks

Familiarity with streaming and event-driven architectures using Kafka

Requirements

We are seeking an experienced Senior Data Engineer to design, build, and operate enterprise-scale data pipelines and datasets that support advanced analytics, enterprise reporting, and machine learning use cases. You will play a key role in shaping the Enterprise Data Strategy and Common Data Model while working closely with data scientists, analysts, and business stakeholders in an Agile environment. Key Responsibilities Design, build, and maintain datasets and data pipelines for analytics and data science use cases Prepare, troubleshoot, and optimize data pipelines to ensure reliability, performance, and data quality Co-design and evolve the Enterprise Data Strategy and Common Data Model Implement and operate core Data Platform processes and services Develop and maintain data pipelines tailored for Data Scientists and analytics teams Maintain and govern model JSON schemas, metadata, and data definitions Identify, analyze, and resolve data quality and data consistency issues Support : Enterprise reporting and analytics Machine Learning operations (MLOps) Collaborate with stakeholders across IT, data science, analytics, and business domains using Agile delivery methods Required Experience Proven experience working with Big Data platforms and technologies, including : S3, Hive, Spark, Trino, MinIO Kubernetes (K8S) and Kafka Strong experience with SQL-based and relational data systems, including PL / SQL Experience handling banking or financial data, including governance and regulatory considerations Hands-on experience with large-scale on-premises Data Lake migrations Integration of Data Science workbenches such as : KNIME, Cloudera, Dataiku (or similar platforms) Experience working in Agile environments (Scrum, SAFe) Proven stakeholder management and cross-team collaboration skills Technical Skills Strong understanding of enterprise data reference architectures Expert-level SQL and data modeling skills Python for : Automation Data processing Notebooks and analytics workflows Data preparation techniques for : Reporting Advanced analytics Machine learning Experience implementing and working with data quality frameworks Familiarity with streaming and event-driven architectures using Kafka