About the role

We are building a data platform that lets teams use sensitive data without exposing the people in it. We are hiring a Senior Data Architect to own that platform end to end: from the raw zone in S3, through de-identification, to a shared zone that analysts, data scientists and ML engineers can query safely.

You will set the architecture and write the standards; other engineers will build to them, often without you in the room. The role sits between data engineering, security and privacy, so you will work daily with our security engineer and with legal and compliance.

Location: Noida• Reports to: VP Data Team• Level: Senior / Principal

What you will do

- Design the platform end to end. Define the zone model, storage layout, Parquet partitioning and compaction strategy, and decide whether a table format such as Iceberg earns its complexity here. You will make that call and defend it.
- Write the de-identification standard. Specify which technique applies to which class of field, the generalization hierarchies, and the k-anonymity thresholds for quasi-identifier combinations. The standard must be accurate enough for engineers to implement without you.
- Design integration from source systems. Decide how data moves from our transactional systems and our existing Athena lake into the platform, change data capture or batch, and argue the case for your choice.
- Choose tokenization and key management. Select the tokenization technology and design key management together with our security engineer.
- Own catalog, lineage and schema evolution. This includes the fail-closed rule: no unclassified column ever reaches the shared zone.




- Set and meet the targets that matter. Freshness, cost per terabyte, and query performance for the teams consuming the data.

What we are looking for
- About eight years in data engineering or architecture, including at least three designing platforms that other engineers built on.
- Hands-on AWS lakehouse experience with S3, Glue, Athena and Lake Formation, or close equivalents. You know Parquet well: partitioning, file sizing and compaction, and what goes wrong when they 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.
- Extraction design from transactional systems. You have built it and can argue change data capture versus batch from experience.
- A technical standard you wrote that other people followed. We will ask to see one.
- Real work 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, not only in a paper.
- Data catalog and lineage tooling.
- Cost engineering on a large lake: you know where the money actually goes.
- Enough machine-learning exposure to know which properties of a dataset model training depends on.

How we hire We will ask to see a technical standard you wrote, and to hear how you got other engineers to follow it. Bring one you can share, redacted if needed.Role & responsibilities

Preferred candidate profile

📌 Data Architect (Noida)
🏢 Wildnet Technologies
📍 Noida