Roles We Fill
Data engineering roles, precisely defined
Generalist recruiters search by tool names. We search by what the platform must do: move data reliably, store it in a form people trust, and serve it in real time when the business cannot wait.
Pipelines
Getting data in, cleaned, and on time
Data Pipeline Engineer
Builds and orchestrates batch ETL and ELT workflows, with testing, retries, and lineage built in from the start.
Data Quality & Observability Engineer
Owns freshness, accuracy, and schema-change monitoring so broken data is caught before it reaches a model or a report.
Data lakes
Storing data so it stays usable
Lakehouse Engineer
Builds open table formats, partitioning, and storage layers that balance query speed against cloud cost.
Data Platform Architect
Designs the target architecture across ingestion, storage, modeling, access control, and governance.
Real-time streaming
Acting on data as it arrives
Streaming Data Engineer
Builds event-driven pipelines with brokers and stream processors, handling ordering, late data, and exactly-once semantics.
Real-Time Platform Lead
Runs the streaming platform end to end, from capacity and latency targets to on-call and incident response.
Head of Data Engineering
Sets the data platform roadmap and owns build-vs-buy decisions across pipelines, storage, streaming, cost, and team structure.
Why Propellence
Built for a search problem that crosses layers
Most data engineering searches stall because the recruiting partner sources by tool keyword. The right candidate often sits across batch and streaming, or between engineering and architecture.
Bridge talent, not keyword matches
We identify engineers who understand data modeling and distributed systems, not only one vendor stack.
One search, three layers
Pipelines, lakes, and streaming, evaluated by consultants with real technical grounding in each.
Confidential, passive search
Senior data engineers are rarely on job boards. Our search is direct, discreet, and relationship-driven.
Global reach
Active coverage across India, Germany, the USA, Japan, and APAC, where product companies and GCCs are scaling data platforms.
FAQ
Data engineering talent acquisition, explained
Straight answers to what clients ask before starting a search.
What does data engineering talent acquisition cover?
It covers hiring for data pipelines, data lakes and lakehouses, and real-time streaming systems, plus the data quality, architecture, and platform leadership roles that surround them.
Why is data engineering hiring hard?
Strong data engineers combine software engineering, distributed systems knowledge, and data modeling. Most candidates are strong in one area, and few have run pipelines reliably at scale under real cost and latency pressure.
Do you hire for real-time streaming roles?
Yes. We search for streaming data engineers and real-time platform leads for product companies, ER&D teams, and GCCs across India, Germany, the USA, Japan, and APAC.
How do you assess data engineers beyond tool names?
Our consultants screen for the specifics: data volumes and latencies handled, architecture decisions owned, incidents resolved, and cost improvements delivered, so you meet people who have operated platforms, not only used them.
Can you support a full data platform team build-out?
Yes. From one critical architect to coordinated multi-hire team builds across pipelines, lakes, and streaming, with the same rigor we bring to GCC build-outs.
Scaling a data platform team?
Tell us which layers are missing. We will bring you candidates who bridge them, not just fill a title.