Strong proficiency in SQL and database development (relational + NoSQL)
Experience with data engineering languages such as Python
Proven experience building ETL/ELT data pipelines using tools such as: GCP Dataflow, DataForm/dbt, BigQuery Azure Data Factory, Synapse, Databricks AWS Glue / Lambda
Solid understanding of database design principles, including relational databases and star schema structures
Proven ability to work effectively with diverse data types, from structured datasets to semi-structured and unstructured sources
Strong data analysis skills, including profiling, assessing data quality, and identifying patterns to inform data modelling and pipeline design
Skilled in testing data pipelines, validating transformations, and ensuring data quality through automated and manual testing methods
Experience with Git, DevOps pipelines
Strong understanding of security, access control, and compliance standards
Experience working with graph databases (e.g. Google Spanner, Neo4j, Gremlin) is desirable
Experience performing Data engineering on GCP is desirable
Familiarity with containerisation and orchestration (Docker, Kubernetes) is desirable
Telecommunications industry experience is desirable
Experience with real‑time streaming and event-driven architectures is desirable
Experience with agent development and integrating data systems via frameworks like MCP is desirable.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLPythonETLELTdata engineeringdata analysisdatabase designdata qualitytesting data pipelinesreal-time streaming

Soft Skills

collaborationproblem-solvingcommunication