Tier 1 Investment Bank
Accelerating data engineering delivery for a global investment bank Building high-performing Snowflake and Databricks squads to strengthen data, analytics and reporting across Global Banking & Markets.
(3-minute read)
Accelerating data engineering delivery for a global investment bank Building high-performing Snowflake and Databricks squads to strengthen data, analytics and reporting across Global Banking & Markets.
(3-minute read)
A major Tier 1 investment bank required specialist data engineering capability to accelerate delivery across its Global Banking & Markets function. While the bank had strong strategic leadership, it lacked the in-house Snowflake, Databricks and cloud engineering expertise needed to build high-quality data pipelines at speed. Multiple transformation initiatives, including regulatory reporting, trading analytics, risk modelling and financial insights, relied on data that was fragmented across legacy systems, inconsistent in quality, and slow to process. Traditional hiring routes were too slow, and existing consulting partners could not mobilise the specialist skills required in time. G&F were engaged to rapidly build and embed specialist data engineering squads, strengthen cloud-native data platforms and uplift delivery across business-critical projects.
In this case study
● Mobilising elite Snowflake and Databricks engineering squads
● Modernising pipelines, models and data quality
● Strengthening delivery capability across Global Banking & Markets
G&F mobilised a team of specialist data engineers within two weeks, integrating seamlessly into the bank’s existing programme structure. Working alongside the consulting partner already in place, we upskilled squads, introduced clearer sprint discipline, and improved engineering consistency across teams.
We brought deep expertise across Snowflake, Databricks, PySpark, SQL, Airflow and Azure cloud services, enabling the bank to accelerate delivery on high-priority initiatives without compromising quality or governance.
This rapid mobilisation ensured critical GBM programmes could move forward at pace, despite internal hiring constraints.
Our consultants improved the reliability, performance and governance of the bank’s data ecosystem by uplifting pipelines and rebuilding key data flows across trading, risk and financial reporting domains.
These improvements reduced friction across downstream analytics teams and strengthened the bank’s ability to produce consistent, high-quality insight.
G&F supported both engineering and programme leadership, improving cohesion between technology and business stakeholders. We enhanced delivery maturity through better rituals, clearer prioritisation and more predictable sprint outcomes.
Our engineers also introduced cloud-native best practices across deployment, version control and environment management, enabling smoother collaboration across squads and reducing rework caused by inconsistent engineering patterns.
Together, these improvements provided the bank with a more scalable, sustainable and high-performing data engineering capability.
Snowflake, Databricks and cloud-native engineering expertise deployed within two weeks.
Improved data quality, stronger models and higher-performing pipelines across GBM.
Clearer development standards, better sprint discipline and improved cross-team coordination.
Stronger internal capability and a scalable model to support future programmes.
