Senior Engineer at Wood Mackenzie in Edinburgh, Scotland, United Kingdom | Kablio

Role

Description

data governance

ingestion pipelines

self-service

test suites

data quality

distributed computing

Govern and manage large datasets across AWS environments, including data versioning and resolving quality issues for internal users
Own and continuously improve data ingestion and transformation pipelines for large-scale climate and renewables datasets (weather data, renewable generation data), ensuring quality and timely delivery across the business
Facilitate cross-functional data exchange by ingesting and transforming datasets from other engineering teams and delivering them to stakeholders in their required formats
Build and deploy self-service infrastructure components (AWS Lambda functions, Glue/Athena tables, computing infrastructure) that make data access and preparation seamless for data scientists
Build and maintain robust unit and BDD (Behave) test suites that validate complex transformation and modeling logic
Mentor data scientists on code structure, effective testing practices, and engineering standards
Work closely with internal teams and end-users to understand their needs, address technical challenges, and co-design scalable architectural solutions that serve the broader organization
Define and implement comprehensive data quality assurance processes, including validity checks and proactive diagnosis and resolution of production issues
Partner with data scientists to develop new code and scripts, refactoring them into maintainable, efficient, and reusable functions that prevent future bottlenecks
Design and implement optimization strategies for large-scale data processing and complex modeling tasks, leveraging parallelization and distributed computing tools like Dask for maximum performance and efficiency
Create shared code frameworks, templates, and internal libraries that enforce best practices, contribute to company-wide tooling, and accelerate data science workflows

Requirements

aws

python

pyspark

ci/cd

data engineering

leadership

Strong hands-on experience with AWS services for data processing (Step Functions, Lambda, Batch, S3, Athena)
Deep knowledge of software engineering best practices, including design patterns, refactoring, infrastructure as code, containerization, and CI/CD pipelines
High-Performance Engineering & Code Acceleration (35%)
Proven experience writing comprehensive test suites (unit, integration, and behavioural/Behave tests)
Architectural Leadership & Collaboration (10%)
Data Ingestion, Transformation, Delivery & Maintenance (35%)
Experience in Data Engineering or software engineering supporting data science or research teams
Advanced Python proficiency and expert-level experience with distributed computing frameworks (e.g., PySpark, Dask)

Benefits

Information not given or found

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found

Company

Overview

Founded in 1923

Year of establishment

The company was originally established as a stockbroker in Edinburgh.

$2.8B acquisition in 2015

Acquisition value

The company was acquired for approximately $2.8 billion in 2015.

2023 Veritas Capital

Recent ownership change

The company joined Veritas Capital in 2023, marking a new growth phase.

Pivoted in 1973 to pioneering energy research with its first oil report.
Over five decades evolved into a global consultancy powering decisions in energy, chemicals, metals, mining and renewables.
Its Lens platform spans power, hydrogen, carbon, LNG, maritime and more—turning vast datasets into strategic foresight.
Typical projects include asset valuation, project economics, supply‑chain intelligence and portfolio optimization.
With 30+ offices and a presence across energy value chains, it guides governments, producers and financial institutions.
Stands out by integrating legacy upstream expertise with cutting‑edge analytics across renewables and transition fuels.
Notable for weaving real‑time vessel tracking and carbon insights alongside decades‑deep commodity research.

Culture + Values

We operate with integrity and accountability.
We are ambitious, agile, and focused on delivering value.
We are collaborative and work as one team.
We foster innovation, curiosity, and continuous learning.
We aim to be customer-focused, always striving for excellence.

Environment + Sustainability

Net Zero by 2050

Sustainability Goal

Focus on achieving carbon neutrality by the year 2050 as part of global efforts to combat climate change.

Committed to supporting the energy transition and global sustainability goals.
Provide insights to help clients meet their own sustainability goals.
Reduce environmental impact through operational efficiency and technology.

Inclusion & Diversity

40% Target

Female Leadership Representation by 2025

Striving for balanced leadership in senior roles to foster gender equity within the organization.

37% Workforce

Women Globally in 2023

Reflecting the current composition of the global workforce, highlighting progress toward diversity goals.

Committed to creating an inclusive workplace where everyone feels valued and empowered.
Focus on hiring and retaining diverse talent across all levels of the company.

Articles

Wood Mackenzie Careers

Wood Mackenzie Insights

About Wood Mackenzie

Role

Description

data governance

ingestion pipelines

self-service

test suites

data quality

distributed computing

Govern and manage large datasets across AWS environments, including data versioning and resolving quality issues for internal users
Own and continuously improve data ingestion and transformation pipelines for large-scale climate and renewables datasets (weather data, renewable generation data), ensuring quality and timely delivery across the business
Facilitate cross-functional data exchange by ingesting and transforming datasets from other engineering teams and delivering them to stakeholders in their required formats
Build and deploy self-service infrastructure components (AWS Lambda functions, Glue/Athena tables, computing infrastructure) that make data access and preparation seamless for data scientists
Build and maintain robust unit and BDD (Behave) test suites that validate complex transformation and modeling logic
Mentor data scientists on code structure, effective testing practices, and engineering standards
Work closely with internal teams and end-users to understand their needs, address technical challenges, and co-design scalable architectural solutions that serve the broader organization
Define and implement comprehensive data quality assurance processes, including validity checks and proactive diagnosis and resolution of production issues
Partner with data scientists to develop new code and scripts, refactoring them into maintainable, efficient, and reusable functions that prevent future bottlenecks
Design and implement optimization strategies for large-scale data processing and complex modeling tasks, leveraging parallelization and distributed computing tools like Dask for maximum performance and efficiency
Create shared code frameworks, templates, and internal libraries that enforce best practices, contribute to company-wide tooling, and accelerate data science workflows

Requirements

aws

python

pyspark

ci/cd

data engineering

leadership

Strong hands-on experience with AWS services for data processing (Step Functions, Lambda, Batch, S3, Athena)
Deep knowledge of software engineering best practices, including design patterns, refactoring, infrastructure as code, containerization, and CI/CD pipelines
High-Performance Engineering & Code Acceleration (35%)
Proven experience writing comprehensive test suites (unit, integration, and behavioural/Behave tests)
Architectural Leadership & Collaboration (10%)
Data Ingestion, Transformation, Delivery & Maintenance (35%)
Experience in Data Engineering or software engineering supporting data science or research teams
Advanced Python proficiency and expert-level experience with distributed computing frameworks (e.g., PySpark, Dask)

Benefits

Information not given or found

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found

Company

Overview

Founded in 1923

Year of establishment

The company was originally established as a stockbroker in Edinburgh.

$2.8B acquisition in 2015

Acquisition value

The company was acquired for approximately $2.8 billion in 2015.

2023 Veritas Capital

Recent ownership change

The company joined Veritas Capital in 2023, marking a new growth phase.

Pivoted in 1973 to pioneering energy research with its first oil report.
Over five decades evolved into a global consultancy powering decisions in energy, chemicals, metals, mining and renewables.
Its Lens platform spans power, hydrogen, carbon, LNG, maritime and more—turning vast datasets into strategic foresight.
Typical projects include asset valuation, project economics, supply‑chain intelligence and portfolio optimization.
With 30+ offices and a presence across energy value chains, it guides governments, producers and financial institutions.
Stands out by integrating legacy upstream expertise with cutting‑edge analytics across renewables and transition fuels.
Notable for weaving real‑time vessel tracking and carbon insights alongside decades‑deep commodity research.

Culture + Values

We operate with integrity and accountability.
We are ambitious, agile, and focused on delivering value.
We are collaborative and work as one team.
We foster innovation, curiosity, and continuous learning.
We aim to be customer-focused, always striving for excellence.

Environment + Sustainability

Net Zero by 2050

Sustainability Goal

Focus on achieving carbon neutrality by the year 2050 as part of global efforts to combat climate change.

Committed to supporting the energy transition and global sustainability goals.
Provide insights to help clients meet their own sustainability goals.
Reduce environmental impact through operational efficiency and technology.

Inclusion & Diversity

40% Target

Female Leadership Representation by 2025

Striving for balanced leadership in senior roles to foster gender equity within the organization.

37% Workforce

Women Globally in 2023

Reflecting the current composition of the global workforce, highlighting progress toward diversity goals.

Committed to creating an inclusive workplace where everyone feels valued and empowered.
Focus on hiring and retaining diverse talent across all levels of the company.

Articles

Wood Mackenzie Careers

Wood Mackenzie Insights

About Wood Mackenzie