Specialities · Data Engineering and Cloud
Data engineering and cloud recruitment for financial services and GCCs
From data engineers and data architects to cloud engineers, cloud architects and platform automation specialists, we help insurers, banks, consultancies and global capability centres hire the people who design, build and run the data infrastructure that analytics and AI depend on. India is at the centre of this build out, and so are we.
What data engineering and cloud roles does EliteRecruitments recruit for?
EliteRecruitments recruits across the full data infrastructure stack: data engineers and big data engineers, ETL and data pipeline developers, data warehousing specialists, data modelling and data architecture professionals, cloud engineers and cloud architects, and the platform and automation engineers who keep it all running. It serves insurers, banks, consultancies and global capability centres, where data platform hiring is among the most competitive in the technology market.
Since 2015
recruited within our analytics and data practice
80%
screening ratio, candidates we submit get interviewed
85%
joining ratio, offers that become joiners
GCC led
where the strongest platform build out demand sits
The market
The hardest technical profiles to hire
Every analytics model, every AI system and every regulatory report in a financial services firm runs on data infrastructure that someone had to design, build and operate. As firms move their estates to the cloud and modernise legacy warehouses, the people who can do that work well have become some of the hardest technical profiles to hire. The demand is not only for coders. It is for engineers and architects who understand data as a product, who can design for governance and scale, and who know what a regulated financial services environment requires.
We recruit these roles as part of our analytics and data practice, which we have run since 2015. That matters because data engineering hiring goes wrong when the recruiter cannot tell a genuine platform builder from a tool user, and because infrastructure hires only succeed when they fit the analytics and AI functions they serve. We understand both sides of that line.
The data engineering and cloud roles we recruit
We recruit the whole platform layer
Data infrastructure is not one job family. It spans the engineers who build pipelines, the specialists who model and store data, the architects who design the estate, the cloud professionals who run it at scale, and the automation engineers who keep it reliable. Together this is data platform engineering: the layer that makes AI, fraud detection, pricing, customer analytics and regulatory reporting possible. For data science, machine learning, Gen AI and applied analytics, see our Analytics and AI practice, which this page partners with.
Data engineering
Data engineers, senior data engineers and big data engineers who build the platforms that make fraud detection and AML monitoring, pricing, claims analytics and regulatory reporting possible. They design batch and streaming pipelines on frameworks such as Spark, Kafka and Airflow, and on platforms such as Databricks, moving pricing, claims, risk and regulatory data reliably at scale. The highest volume cluster in the market and the foundation of every data function.
Cloud engineering
Cloud engineers and cloud data specialists who build and operate scalable infrastructure across AWS, Azure and Google Cloud: infrastructure as code with Terraform, container platforms such as Kubernetes, and the storage layers, from S3 and Azure Data Lake to Google Cloud Storage, with the security and cost discipline regulated firms require. As financial services estates move to the cloud, these roles have moved from specialist to essential.
Data modelling and data architecture
Data modellers, data architects and enterprise data architects who design how data is structured, related and governed: dimensional and semantic modelling in the Kimball and Inmon traditions, Data Vault, canonical models and domain design, and modern patterns such as lakehouse, medallion architecture, data mesh and data fabric. Judgement heavy roles where a wrong hire shapes everything built afterwards, and where our screening depth earns its keep.
Data warehousing and big data
Data warehousing specialists and big data engineers who build and run the warehouses, lakehouses and large scale data stores behind risk reporting, Basel and IFRS 17 workloads and enterprise analytics, on platforms such as Snowflake, Databricks, BigQuery, Amazon Redshift, Azure Synapse and Microsoft Fabric. Warehouse modernisation, from legacy on premise estates to cloud native platforms, is one of the most common mandates we see.
ETL and data integration
ETL and ELT developers, data integration specialists and data pipeline engineers who connect the systems a financial services firm actually runs on: policy administration, core banking, claims, finance, AML and risk systems all feed the data estate, and integration is what makes customer 360, fraud monitoring and regulatory reporting possible. Mandates commonly involve Azure Data Factory, Informatica, Talend and Airflow alongside cloud native tooling.
Cloud architecture
Cloud architects and solution architects who design cloud estates end to end: landing zones, migration strategy, multi cloud and hybrid design, and the security and compliance architecture that regulated firms require. These senior hires shape everything built after them, which is why clients ask us to search rather than advertise.
Platform engineering, automation and DevOps
Platform engineers, DevOps and DataOps specialists and automation engineers who build the CI CD pipelines, orchestration, monitoring and internal platforms that data and engineering teams depend on. Automation is what turns a collection of pipelines into a production system a bank or insurer can rely on. For general application DevOps and software engineering, see our IT and Technology practice.
Modern data platforms
Much of today’s hiring is organised around the major platforms themselves: Databricks, Snowflake, Microsoft Fabric, BigQuery, Amazon Redshift and Azure Synapse. Clients increasingly brief us by platform, a Databricks migration team, a Snowflake modernisation, a Fabric rollout, and we map and screen candidates by genuine platform depth, not certificate collections.
Data platform leadership
Heads of data engineering, heads of data platform, cloud and infrastructure leaders and data platform transformation leads, the senior roles that own the infrastructure function and its roadmap. These sit alongside our executive search work and are often the first hire in a new capability centre build.
Where the platform build out is happening
India’s capability centres are building serious infrastructure
The strongest demand for data engineering and cloud talent we see is inside India’s global capability centres. International insurers, banks and consultancies are building serious data infrastructure in their India centres, enterprise warehouses, cloud platforms and the automation around them, not just support functions. Reported industry analysis suggests that for every core AI specialist a capability centre hires, it needs several people in the surrounding data and platform roles, and those platform roles are precisely the ones on this page. The scarcest profiles are engineers and architects with the experience to design and run production grade systems in a regulated environment, and that is the band our mapping led process is built to reach.
From the first platform lead through the full build
We help these centres hire from the first platform lead through the full build, and because we also recruit the analytics, data science and AI teams that sit on top, we can build the whole function rather than one layer of it.
How we assess
Tool lists say very little
Data engineering is a field where tool lists on a CV say very little. Anyone can name Spark, Snowflake or Terraform. What matters is whether an engineer has actually designed, built and operated systems at scale, made the trade offs, handled the failures and lived with the consequences of their architecture. That is what our screening tests. We assess real build and run experience, depth on the platforms a mandate needs, and the communication and fit that decide whether a hire lasts.
That discipline is why our placements last. We screen at an 80 per cent rate and our candidates join at an 85 per cent rate, and since 2014 we have had to return fewer than fifteen fees because a placement left early. In a market where strong platform engineers hold several offers at once, that retention record is hard won and rare. See how we work →
Your specialist
Led by a founder who stays close to the work
Founder led
Shagun Gupta
Founder, stays close to the specialist work
EliteRecruitments is led by its founder, Shagun Gupta, who built the firm out of actuarial and stays close to the specialist work across the practice. Data engineering and cloud is recruited within our analytics and data practice, led by Kanishka Gupta, so contact routes to people who understand both the platform layer and the analytics and AI functions it serves. When you talk to us about a data engineering or cloud role, you reach people who understand the work and the market for it, not a generalist passing on a brief.
Practice and proof
One practice, both layers
Data engineering and cloud is recruited within our analytics and data practice, led by Kanishka Gupta under our founder Shagun Gupta, so the people who recruit your platform team are the same people who understand the analytics and AI functions it will serve.
Proof from our work
Insurance grade data engineering, several hires placed at once within about four weeks, all with genuine insurance domain backgrounds.
More examples are on our Success Stories page.
How to work with us
Four ways in, one 90 day guarantee
We recruit data engineering and cloud talent on the basis that suits the role: capability build recruitment when you are standing up a platform function from scratch, retained search for architects and leadership, contingency for active roles, and recruitment process outsourcing at volume.
Whichever way we work, our placements come with a 90 day guarantee. If a placement does not work out within 90 days, we replace them. Since 2014 we have had to return fewer than fifteen fees, because our process is built so the guarantee rarely needs to be used. Explore Capability Build Recruitment →
Common questions
Frequently asked questions
What does a data engineer do?
What is ETL?
What is data architecture?
What is cloud engineering?
What data engineering roles does EliteRecruitments recruit for?
Do you recruit cloud engineers and cloud architects?
Do you cover data warehousing and big data platforms?
How is this different from your Analytics practice?
Do candidates pay any fees?
Start a conversation
Talk to us about data engineering and cloud hiring
Whether you are building a data platform in a capability centre, hiring a single hard to find data architect or cloud engineer, or weighing your own next move, we would like to help.
Thank you, we will be in touch shortly.
Your note goes to Kanishka and Shagun directly.