Position Overview

As a Data Engineer, you will design, build, and evolve a cloud‑native data platform that enables reliable analytics, data products, and machine‑learning use cases at scale. You will work closely with analytics, developers, and business teams to deliver high‑quality, well‑governed data that is easy to consume, cost‑efficient, and secure.

Key Responsibilities

Design, build, and operate scalable, reliable, and observable data pipelines using modern ELT and streaming patterns.
Develop and maintain data models that support analytics, reporting, and machine‑learning workloads.
Maintain the end‑to‑end data lifecycle, from source ingestion to consumption, including documentation and data contracts.
Enable and support new inbound and outbound data integrations.
Collaborate closely with analysts, data scientists, and application developers to deliver trusted, self‑service data products.
Implement data quality, validation, and observability frameworks to ensure reliability and transparency.
Monitor pipeline health, performance, and cost; continuously optimize queries, storage, and compute usage.
Apply software engineering best practices (version control, CI/CD, testing, code reviews) to data codebases.
Ensure security, privacy, and regulatory compliance through access controls, auditing, and platform standards.
Contribute to architecture decisions, standards, and technical documentation to support long‑term scalability.
Support backup, disaster recovery, and resilience strategies for critical data assets

Requirements

Strong passion for automation and platform thinking.
Product‑ and outcome‑oriented mindset, with focus on enabling value
Excellent analytical and problem‑solving skills with a high attention to detail.
Clear communicator, able to explain data and technical concepts to non‑technical stakeholders.
Collaborative team player comfortable working across disciplines & locations.
Curious, proactive, and committed to continuous learning and improvement.
Strong prioritization, time‑management, and ownership mindset.
Proven experience as a Data Engineer or similar role.
Strong proficiency in Python and SQL
Experience with distributed data processing (e.g. Spark).
Knowledge of data storage formats and technologies, such as Avro, Parquet and Delta Lake.
Experience designing and operating automated data pipelines and orchestration (e.g. Databricks or Fabric).
Solid understanding of data modelling, analytics engineering, and data warehousing best practices.
Experience with end-to-end analytics platforms such as Microsoft Fabric, Databricks or Snowflake
Experience working with Git and CI/CD pipelines for data workloads

Preferred Qualifications:

Experience working with event streaming technologies such as Kafka
Experience with cloud infrastructure, containerization technologies, and infrastructure-as-code solutions like Terraform.
Familiarity with data governance concepts such as data contracts, lineage, and cost control.
Familiarity with agile methodologies and practices.
AWS Certified Data Engineer or Azure Data Engineer certification

The above duties provide a generic description of the Employee’s day to day responsibilities but should in no way be deemed to be an exhaustive list. Additional related, duties may be assigned by the Line Manager in line with business exigencies and continuity.

Why Work With Us?

At Silverspin, we take bold but thoughtful approaches to what we build and how we work. We act with confidence and responsibility, focusing on smart decisions, meaningful impact, and long-term success.

Joining us means being part of a team that values curiosity, ownership, and collaboration. We encourage people to challenge ideas, simplify complexity, and grow together - creating an environment where great work (and good energy) go hand in hand.

We offer:

Competitive salary and benefits
Opportunities to learn, grow, and develop
A creative, collaborative team environment

Silverspin is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.