experience in data engineering or a related field, with a focus on building scalable data systems and platforms. Expertise in modern data tools and frameworks such as Spark, dbt, Airflow, Kafka, Databricks, and cloud-native services (AWS, GCP, or Azure) Understanding of data modeling, distributed systems, ETL/ELT pipelines, and streaming architectures Proficiency in SQL and at least More ❯
REST APIs and integration techniques Familiarity with data visualization tools and libraries (e.g. Power BI) Background in database administration or performance tuning Familiarity with data orchestration tools, such as ApacheAirflow Previous exposure to big data technologies (e.g. Hadoop, Spark) for large data processing Strong analytical skills, including a thorough understanding of how to interpret customer business requirements More ❯
in data engineering or a related field, with a focus on building scalable data systems and platforms. Strong expertise with modern data tools and frameworks such as Spark , dbt , Airflow , Kafka , Databricks , and cloud-native services (AWS, GCP, or Azure). Deep understanding of data modeling , distributed systems , streaming architectures , and ETL/ELT pipelines . Proficiency in SQL More ❯
to talk to you if: You've led technical delivery of data engineering projects in a consultancy or client-facing environment You're experienced with Python, SQL, .NET, dbt, Airflow and cloud-native data tools (AWS, GCP or Azure) You have strong knowledge of data architecture patterns - including Lakehouse and modern warehouse design (e.g. Snowflake, BigQuery, Databricks) You know More ❯
oversight across the data platform, including data pipelines, orchestration and modelling. Lead the team in building and maintaining robust data pipelines, data models, and infrastructure using tools such as Airflow, AWS Redshift, DBT and Looker.Ensuring the team follows agile methodologies to improve delivery cadence and responsiveness. Contribute to hands-on coding, particularly in areas requiring architectural input, prototyping, or … mentoring skills and ability to foster team growth and development Strong understanding of the data engineering lifecycle, from ingestion to consumption Hands-on experience with our data stack (Redshift, Airflow, Python, DVT, MongoDB, AWS, Looker, Docker) Understanding of data modelling, transformation, and orchestration best practices Experience delivering both internal analytics platforms and external data-facing products Knowledge of modern More ❯
