- Design the platform end to end: the zone model, storage layout, Parquet partitioning and compaction, and the call on whether a table format such as Iceberg earns its complexity here.
- Write the de-identification standard — which technique applies to which class of field, generalization hierarchies, and k-anonymity thresholds for quasi-identifier combinations. Engineers will implement it without you in the room.
- Design integration from our transactional systems and our Athena lake: change data capture or batch, and the argument for which.
- Choose the tokenization technology, and design key management with our security engineer.
- Own catalog, lineage and schema evolution — including the fail-closed rule that stops an unclassified column ever reaching the shared zone.
- Set the targets that matter: freshness, cost per terabyte, and query performance for the teams consuming the data.
What we are looking for
- Around eight years in data engineering or architecture, including three or more designing platforms that other engineers built on.
- Hands-on AWS lakehouse work — S3, Glue, Athena,
Lake Formation or close equivalents — with real Parquet fluency: partitioning, file sizing, compaction, and what happens when those are wrong.
- Practical experience of at least one de-identification discipline: tokenization and vaulting, format-preserving encryption, or statistical disclosure control such as k-anonymity and generalization.
- You have designed extraction from transactional systems and can argue change data capture versus batch from experience.
- You have written a technical standard that other people followed. We will ask to see one.
- You have worked alongside privacy, legal or compliance on something that mattered.
Nice to have
- Work under GDPR, or under an African data protection regime such as Nigeria's NDPA or Kenya's DPA.
- Differential privacy or synthetic data generation in production rather than in a paper.
- Data catalog and lineage tooling.
- Cost engineering on a large lake — knowing where the money actually goes.
- Enough machine-learning exposure to know which properties of a dataset training depends on.
📌 Sr. Data Architect (Noida)
🏢 Wildnet Technologies
📍 Noida
Reply to this offer
Impress this employer describing Your skills and abilities, fill out the form below and leave Your personal touch in the presentation letter.