Description

Senior Data Engineer & Technical Lead with 7+ years building petabyte-scale data platforms on GCP and AWS. I design, build, and run cloud-native data infrastructure, streaming pipelines on Beam/Dataflow and Kafka, batch workloads on Spark/Databricks, Airflow orchestration on Kubernetes.

Focus: cost optimisation, platform reliability, PII governance, migration delivery.

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SELECTED OUTCOMES

- 50%+ cost reduction on an over-provisioned Dataflow streaming pipeline, no latency impact (N Brown Group)

- 90% lower cost than AWS CloudWatch via a custom S3-access-log analytics solution (Bolt)

- 50% runtime reduction migrating a 5B+ row Scala Spark pipeline to Dataform/BigQuery (SoundCloud, via Zencore)

- Greenfield real-time parts-tracking platform with no-loss/no-duplicate guarantees, rolled out across 30+ plants (Renault Industry 4.0)

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STACK

Cloud: GCP (BigQuery, Dataflow, Pub/Sub, GKE, Cloud Run), AWS (S3, Glue, Redshift, SQS, SNS)

Data: Apache Spark (Scala/PySpark), Apache Beam, Kafka + Debezium, Databricks, Delta Lake

Orchestration: Airflow, Dataform, Composer

Infra: Terraform, Kubernetes

Languages: Scala, Java, Python, SQL

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CERTIFICATIONS

- Google Cloud Professional Data Engineer (2026)

- Google Cloud Associate Cloud Engineer (2021–2024)

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LANGUAGES

English (fluent) · German (C1) · Romanian (native)

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Currently engaged as Technical Lead on a GCP data platform. Open to B2B contract engagements for my next role, DACH region, United Kingdom , EU-wide remote, occasional on-site possible.

📩

Languages

English
Fluent
German
Fluent

Workplace preferences

Can work on-site

Cluj-Napoca (up to 50km)

N Brown Group,
Technical Lead
January 2026 - Today (7 months)
Lead the Castle data platform on Google Cloud, owning ingestion architecture and PII governance across batch and streaming workloads.
• • Cut a chroni cally over-provisioned Dataflow streaming pipeline's cost by 50%+ with no la tency impact, using Streaming Engine, throughput-based autoscaling (replacing a static worker floor) and Dataflow Prime.
• • Re-platformed a legacy, business logic-heavy ingestion codebase into a single config-driven job; new sources now onboard by adding a declarative config.
• • Reduced BigQuery cost by serving source data through external tables over existing Cloud Storage buckets with schema autodetection, preserving an audit and debug trail over financial data.
◦ • Added column-level policy tags for PII, dead-letter queues for batch and stream ing (Dataflow/Beam) jobs, and a backfill and replay mechanism.
Google cloud Apache Beam BigQuery Data Engineer Apache Airflow
Zencore,
Data Engineer
August 2025 - December 2025 (4 months)
Cloud consulting and engineering firm founded by ex-Google engineers and executives, focused on de-risking large-scale GCP migrations.
• • On a Sound Cloud engagement, migrated the main songs ingestion pipeline (Scala Spark on Dataproc, 5B+ rows per run) to an incremental, windowed Dataform/BigQuery pipeline orchestrated with hourly Airflow triggers, reducing computation time by ~50% on fewer resources.
Apache Spark Google cloud BigQuery Scala Apache Airflow
Bolt,
Data Engineer
July 2022 - August 2025 (3 years and 1 month)
Worked across the processing and ingestion teams of a multi-petabyte data plat form.
• • Built an S3-access-log analytics solution to distinguish used vs. unused data in the lake at ~90% lower cost than the native AWS CloudWatch offering.
• • Built CDC pipelines with Debezium and Kafka into S3 and the central cata log, managing consumer offsets and hourly Airflow-orchestrated loads from the last known offset; modelled SCD Type 1 dimensions.
◦ • Administered Databricks workspaces, cluster infrastructure, worker pools and access controls, and ran a self-managed Airflow-on-Kubernetes (Helm) deployment for scheduled Spark workloads.
Apache Spark Databricks Amazon Web Services Apache Kafka Python