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or GCP) and cloud-native BI services. Strong understanding of data warehousing, OLAP, semantic models, and query optimization. Experience working with modern data platforms such as Snowflake, Databricks, Redshift, BigQuery, or Synapse. Soft Skills Excellent communication and collaboration skills. Ability to lead technical teams and manage multiple workstreams. Strong problem-solving, analytical thinking, and decision-making skills. Ability to More ❯

design, and troubleshooting queries to support high-volume data transactions and improve database performance. Managing Cloud Data Resources: Develop and maintain software products utilising GCP services such as PubSub, BigQuery, Cloud Storage, and Dataflow. Contributing to Billing Infrastructure: Building and maintaining a reliable billing architecture within an event-driven environment. Collaborating on Problem-Solving: Partnering with Business Intelligence, infrastructure More ❯

in SQL, NoSQL and data integration technologies. Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud. Experience with data warehousing technologies such as Snowflake, Redshift, BigQuery, Synapse, or Teradata. Skilled in ETL/ELT tools and data pipeline technologies such as Informatica, Talend, Databricks, or Apache tools. Strong understanding of data governance, MDM, metadata management More ❯

HBase, etc. Strong experience with Cloud platforms (AWS/Azure/GCP) and services like: AWS: S3, Glue, EMR, Redshift, Lambda, Kinesis Azure: Data Factory, Synapse, Databricks, ADLS GCP: BigQuery, Dataflow, Pub/Sub Experience with Data Warehouse/Data Lake/Lakehouse design and modeling (Kimball, OLAP, OLTP, Star/Snowflake schemas Proficiency in CI/CD, Git More ❯

Bell’s Data Governance and Data Classification policies. Maintain data dictionary. Maintain business level data model. Recommending and introducing new technology where needed. Core: Cloud data platforms (e.g. Snowflake, BigQuery, Redshift) Data transformation technology such as DBT Visual Studio Code Python CI automation systems such as Jenkins A git-based source control system such as BitBucket Data Warehouse/ More ❯

the following tools and technologies (note we do not expect applicants to have prior experience of all them): Google Cloud Platform for all of our analytics infrastructure dbt and BigQuery SQL for our data modelling and warehousing Python for data science Go to write our application code AWS for most of our backend infrastructure You should apply if: What More ❯

industry best practices to continuously enhance data engineering capabilities. Looking at our current pipeline of work, we can also consider those with an Analytics Engineering lean, with experience within BigQuery (GCP) with data modelling in DBT and mobile/telecoms industry experience would be beneficial. A bit about YOU As much as we just love working with great, fun More ❯

for guidance. Strong SQL skills and understanding of data models and data principles. Experience with mobile-responsive dashboards and self-service analytics. Desirable Experience with cloud platforms (Snowflake, Azure, BigQuery). Python skills. Experience in agile delivery environments. Understanding of GDPR and data governance. BI tool certifications. The BI team has historically been highly technical; you will bring the More ❯

for guidance. Strong SQL skills and understanding of data models and data principles. Experience with mobile-responsive dashboards and self-service analytics. Desirable Experience with cloud platforms (Snowflake, Azure, BigQuery). Python skills. Experience in agile delivery environments. Understanding of GDPR and data governance. BI tool certifications. The BI team has historically been highly technical; you will bring the More ❯

DevOps, integrating automated testing into a CI/CD pipeline using Jenkins or GitLab runner. Experience with either AWS, Azure OR GCP Cloud Good knowledge in GCP Storage Buckets, BigQuery, Dataflow and Cloud Functions (any Cloud). Hands-on experience with one or more programming languages (Java/JavaScript). Experience with non-functional testing using JMeter or similar More ❯

Airflow, or dbt. Strong SQL skills and experience with cloud platforms (Azure preferred). Solid programming background in Python, Scala, or Java. Knowledge of data warehousing solutions (e.g. Snowflake, BigQuery, Redshift). Strong understanding of data governance, security, and compliance (experience within financial services is a plus). Leadership experience, with the ability to mentor, influence, and set technical More ❯

on cloud experience (AWS or GCP) with services for compute, data, and AI/ML. Proven skills in data engineering (ETL pipelines, APIs, SQL/NoSQL, data warehouses like BigQuery/Redshift). Experience with AI agents, LLMs and orchestration frameworks (LangChain, AutoGen, LlamaIndex, etc.). Demonstrated ability to ship production-ready internal tools or SaaS integrations. Strong problem More ❯

technology solutions. Strong programming skills (JavaScript, TypeScript, Python, or similar). Experience with cloud platforms (preferably Google Cloud Platform) and serverless architecture. Advanced SQL and data warehousing skills (e.g. BigQuery). Familiarity with front-end frameworks such as React. Experience with ETL/ELT processes and data visualisation tools (e.g. Looker Studio). Knowledge of AI integrations and agent More ❯

equivalent) - building models, tests, documentation, version control. Understanding of data warehousing concepts (star schemas, snowflake, slowly changing dimensions, partitioning, clustering). Experience working in a modern data stack (e.g. BigQuery, Snowflake, Redshift, Databricks, etc.) Comfortable working downstream (with BI/analytics users) and upstream (pipelines, ingestion) contexts. Familiarity with BI tools (we use Thoughtspot and Power BI). Proficient More ❯

engineering with proficiency in a language such as Python for API development, Data engineering and automation tasks • A background in working with storage solutions such as PostgreSQL, MySQL, and BigQuery • Experience in API development using tools such as FastAPI or Flask, enabling data access and integration across systems • Solid knowledge of cloud platforms (GCP and/or AWS), with More ❯

SQL . Hands-on experience with data orchestration tools (Airflow, dbt, Dagster, or Prefect). Solid understanding of cloud data platforms (AWS, GCP, or Azure) and data warehousing (Snowflake, BigQuery, Redshift). Experience with streaming technologies (Kafka, Kinesis, or similar). Strong knowledge of data modelling, governance, and architecture best practices . Excellent leadership, communication, and stakeholder management skills. More ❯

technical topics for non-technical audiences. Team player - Collaborative, curious, and happy to work in a self-organising, fast-paced environment. Nice-to-Have Skills: Experience with GCP/BigQuery or other cloud data warehouses (e.g., Snowflake, Redshift). Familiarity with data orchestration tools (e.g., Airflow). Experience with data visualisation platforms (such as Preset.io/Apache Superset or More ❯

technical topics for non-technical audiences. ·Team player - Collaborative, curious, and happy to work in a self-organising, fast-paced environment. Nice-to-Have Skills: ·Experience with GCP/BigQuery or other cloud data warehouses (e.g., Snowflake, Redshift). ·Familiarity with data orchestration tools (e.g., Airflow). ·Experience with data visualisation platforms (such as Preset.io/Apache Superset or More ❯

Skills : Advanced hands-on experience with major cloud platforms (Azure, AWS, GCP), including cloud-native data services, orchestration, monitoring, and automation. Proven experience in GCP-based data platforms , including BigQuery, Dataflow, CloudSQL, Spanner, Pub Sub, Cloud composer and GKE. Data Engineering : Proficient in modern data tools and frameworks for building, operating, and optimising ETL/ELT pipelines, streaming data More ❯

with openness to various data platforms including AWS, Azure, and Google Cloud. Strong knowledge of data services across multiple platforms (e.g., AWS S3, Redshift, Glue, Azure Blob Storage, Google BigQuery). Expertise in data modelling (Dimensional, Data Vault, Enterprise). Experience designing and implementing modern data architectures. Proficiency with integration/orchestration tools (Airflow, dbt, Glue). Strong communication More ❯

teams you will model data from business systems to be insight and operations ready. This role is for you if you have: Solid experience in SQL Experience with Google BigQuery preferable Experience working with relational and non-relational data sources Experience using Google Looker Studio (formerly Data Studio), or similar products Data visualisation and data storytelling skills Familiarity with More ❯

Experience Masters degree level Exposure to working with digital analytics tools (e.g. Google Analytics or Adobe Analytics) Knowledge of coding language or modelling tools relevant to data manipulation (e.g. BigQuery, SQL, R, Python, MatLab, Stata) Knowledge of dashboarding solutions (e.g. Power BI, Looker, Tableau) If this sounds like you, please get in touch We look forward to meeting you. More ❯

campaign initiatives. Analyse large datasets using SQL and Python to uncover trends and guide strategic decision-making. Build and maintain KPI dashboards using modern BI tools (e.g., Looker Studio, BigQuery). Maintain high data quality through validation, monitoring and governance best practices. Contribute to the development and optimisation of the analytics and data warehouse environment. Provide BI guidance, training More ❯

investors and the Board Technology Background of the CTO: · Languages: Python, TypeScript/JavaScript (Node.js), Dart (Flutter), SQL/MySQL, JSON, REST, GraphQL · Cloud and Data: Google Cloud Platform (BigQuery, Cloud Run, Firestore, Pub/Sub) · DevOps and Tooling: GitHub, GitHub Actions, Terraform, Docker, Kubernetes · Monitoring: Stackdriver, Grafana, Prometheus · Security: OAuth2, SSO, IAM, ISO27001 alignment, penetration testing · Analytics and More ❯

crucial role in data impact felt throughout the company by: Designing and implementing robust and well-orchestrated E2E data pipelines (ELT, Reverse-ETL). Employing data modelling techniques in BigQuery and dbt, with an emphasis on legal and venture capital data. Building production-grade LLM pipelines with complex business domain understanding. You'll have complete ownership and autonomy over More ❯