Senior Data Engineer

Design, implement, test and maintain scalable and efficient ETL/ELT pipelines to ingest and transform large datasets from diverse sources.

Architect, build, test and optimize data warehouses and data lakes, focusing on data consistency, performance, and best practices for analytics.

Implement data quality checks, validation rules, and monitoring to ensure data accuracy, integrity, and security.

Contribute to data governance initiatives.

Continuously monitor and enhance database and query performance, identifying opportunities to streamline and improve data retrieval times.

Define and design frameworks for monitoring data quality and data pipelines 

Monitor Google BigQuery consumption.

Evaluate, select, and integrate new tools and technologies to enhance data capabilities.

Automate manual processes to improve efficiency and accuracy.

Mentor junior engineers, sharing best practices in data engineering and analysis, and helping to build a culture of technical excellence within the team.

Proactively lead streams of data engineering work, collaborating with stakeholders and other non-engineering roles to come up with creative solutions to the problems your team is presented with.

Work with both data producers and consumers to optimise existing data products and the data within them to meet evolving business needs.

Work collaboratively with other engineers, using techniques like pair and ensemble programming, to foster collective ownership and help upskill, reskill and learn from other team members.

Help guide technology choices, adopting company-standard tech stacks by default while responsibly investigating alternative tools and services and looking for opportunities to innovate.

SQL, Python, Google Cloud Platform (GCP), and Google BigQuery

Data pipeline tools such as Apache Airflow and DBT

Data modeling concepts, including dimensional and star schema design

Data visualization tools like Looker

Several years of experience in Data / Software engineering on a cloud platform.

An understanding of data and distributed systems concepts.

Several years of experience working with data from large ERP systems.

Knowledge of data quality concepts and experience using tools to identify data quality problems and come up with strategies to tackle them.

Experience working with iterative software development principles:

Continuous Integration & Deployment (CI/CD)

Automated testing at different levels

Collaborative development techniques like pair and ensemble programming

Experience owning software systems and/or data pipelines end-to-end, having full responsibility for operating them in production and responding to problems that arise.

The ability to work with stakeholdersand other non-technical roles to translate business requirements into technical work, and conversely to articulate necessary technical work to the same people for prioritisation.

High competence in SQL and deep experience of at least one programming language Python, Java

Experience with decentralised Data Mesh and Data Product architecture principles.

Experience building and testing data pipelines with DBT

Understanding and experience of User-Centric Design principles

Actively contributing to the codebase in collaboration with other engineers and deploying changes into production

Familiarising yourself with our tech stack and processes

Starting to understand the data landscape in and around your team

Getting to know the various stakeholders and their general requirements

Collaborating effectively with each discipline on the team

Participating in technical discussions and sharing ideas

Gaining an understanding of the team’s context within the wider organisation

Leading a stream of work, working with both engineers and stakeholders

Triaging support queries and diagnosing issues in live data pipelines

Setting the technical direction of the work done by the team

Holding discussions within the engineering team in order to improve product architecture and code quality

Ensuring that data is stored securely and in compliance with GDPR

Take ownership of key projects and drive initiatives to enhance data capabilities.

Switching context between multiple streams of work, providing guidance to both developers and stakeholders

Fostering a culture of continuous feedback, giving and receiving constructive feedback within your team, and proactively improving ways of working

Mentoring others in the principles of data engineering and best practices and looking for opportunities to help other engineers on the team grow

Arbitrating disagreements within the team and not avoiding difficult conversations

Gauging the complexity and scope of a piece of work, breaking it into smaller pieces when appropriate with a focus on iteratively delivering value to end users