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.

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
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.

Data Engineer

Key skills