Senior Data Architect
We are seeking an experienced Senior Data Architect to lead the design and implementation of our data architecture strategy.
About the Role
This is a senior-level position that requires a strong technical background and excellent problem-solving skills. You will work closely with cross-functional teams to drive the alignment of data architecture requirements with strategic goals.
Define the overall greenfield data architecture using GCP - BigQuery.
Establish best practices for ingestion, transformation, data quality, and governance.
Lead the design and implementation of ETL/ELT pipelines: Ingestion: Datastream, Pub/Sub, Dataflow, Airbyte, Fivetran, Rivery; Storage & Compute: BigQuery, GCSTransformations: dbt, Cloud Composer (Airflow), Dagster.
Ensure data quality and reliability with dbt tests, Great Expectations/Soda, and monitoring.
Implement Dataplex & Data Catalog for metadata, lineage, and discoverability.
Define IAM policies, row/column-level security, DLP strategies, and compliance controls.
Define and enforce SLAs, SLOs, and SLIs for pipelines and data products.
Implement observability tooling: Cloud Monitoring, Logging, Error Reporting, Cloud Trace.
Build alerting and incident response playbooks for data failures and anomalies.
Ensure pipeline resilience (idempotency, retries, backfills, incremental loads).
Establish disaster recovery and high availability strategies (multi-region storage, backup/restore policies).
Requirements
This role requires:
10+ years experience in data engineering, architecture, or platform roles.
Strong expertise in GCP data stack: BigQuery, GCS, Dataplex, Data Catalog, Pub/Sub, Dataflow.
Hands-on experience building ETL/ELT pipelines with dbt + orchestration (Composer/Airflow/Dagster).
Deep knowledge of data modeling, warehousing, partitioning/clustering strategies.
Experience with monitoring, reliability engineering, and observability for data systems.
Familiarity with data governance, lineage, and security policies (IAM, DLP, encryption).
Strong SQL skills and solid knowledge of Python for data engineering.
Benefits
This is a dynamic team environment where you can leverage your technical skills to make a meaningful impact. If you have a passion for data engineering, this could be the perfect opportunity for you