Sr Specialist, Data Engineering

• Design, build, and maintain production-grade data pipelines in Airflow that ingest data from digital touchpoints into Snowflake.

• Develop modular, tested, and well-documented dbt models that transform raw data into reliable, business-ready datasets — owning the full lifecycle from source definition to exposure.

• Provision and manage cloud data infrastructure (Snowflake objects, Airflow environments, supporting GCP resources) through Terraform, with everything version-controlled and peer-reviewed.

• Implement and uphold data quality, observability, and testing standards across pipelines

• Tune Snowflake performance and manage warehouse cost — clustering, query profiling, resource monitors and treat cost as a first-class engineering concern.

• Operate the on-call and incident response cycle for owned pipelines: triage failures, perform root-cause analysis, write post-mortems, and convert recurring issues into permanent fixes.

• Implement pipelines to platform standards — branching strategy, CI/CD for dbt and Airflow, code review norms, documentation, naming conventions 

• Stay current on the evolving data engineering stack (agentic tooling, streaming patterns, observability frameworks) and bring grounded recommendations on what to adopt and what to skip.

Required Qualifications :

Education: 

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related quantitative discipline.

• A Master's degree in a relevant field is an advantage but not required.

• Equivalent practical experience or demonstrable self-taught expertise will be considered in lieu of formal qualifications.

Experience: 

• 2 years of hands-on experience in a data engineering or analytics engineering role, ideally within a digital or e-commerce environment.

• Proven experience building and maintaining production-grade data pipelines using Apache Airflow.

• Strong working knowledge of Snowflake, including data modelling, performance tuning, clustering, and warehouse cost management.

• Demonstrated experience developing dbt projects — writing modular, tested, and well-documented transformation logic.

• Practical experience using Terraform to provision and manage cloud data infrastructure in a repeatable, version-controlled manner.

• Proficiency in Python for writing data pipelines, custom operators, and utility tooling.

• Strong SQL skills with the ability to write and optimise complex queries across large datasets.

• Comfortable working with Git in a team environment, including branching strategies, pull requests, and code reviews.

• Hands-on experience using agentic coding environments (Claude Code, Cursor, Windsurf, or similar) as a working partner for planning, writing, and refactoring production code scoping multi-step changes, supplying the right context, and course-correcting across files.

• Fluency in driving an AI coding tool across pipeline codebase: scoping changes, providing the right files as context.

Preferred Qualifications :

• Experience working with Google Cloud Platform (GCP) services such as Cloud Storage, GKE

• Familiarity with containerisation using Docker, including writing Dockerfiles and managing containerised workloads.

• Exposure to container orchestration with Kubernetes for deploying and scaling data services.

• Knowledge of streaming or event-driven data architectures using tools like Pub/Sub, or Kinesis.

• Experience with data observability and quality frameworks such as DQ Labs, Elementary, or Great Expectations.

• Familiarity with CI/CD pipelines and DevOps practices applied to data workflows (GitHub Actions, Cloud Build, or similar).

• Familiarity with the emerging pattern of agentic data engineering — using LLM-driven agents or workflows to automate routine pipeline tasks and a point of view on what should and should not be delegated to an agent.

  • Sound judgment on when AI-generated output needs to be challenged, rewritten, or discarded particularly around security, data privacy, PII handling, edge cases, performance, and idempotency.