Convosight
Data Architect
- Compensation
- Not disclosed
- Location
- Remote
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Architecture
- Posted
Skills
Must have
- 5+ years in data architecture/data engineering, with at least 2+ years in an architect or lead capacity.
- Strong SQL: advanced query optimisation, indexing, partitioning strategies.
- Data modeling dimensional modelling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design.
- Cloud data platforms: hands-on with AWS (Redshift, S3 Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow).
- Big data ecosystems: Spark, Hadoop, or Kafka for large-scale/streaming data.
- Data warehousing Snowflake, Redshift, BigQuery, or Databricks.
- ETL/ELT pipeline design: Airflow, dbt, Fivetran, or similar orchestration tools.
- Data governance & security: data lineage, access control, compliance (GDPR/SOC2), master data management.
Full description
Own the end-to-end data architecture design, build, and govern scalable data systems that power analytics, ML, and business decisions across the org.
Requirements:
- 5+ years in data architecture/data engineering, with at least 2+ years in an architect or lead capacity.
- Strong SQL: advanced query optimisation, indexing, partitioning strategies.
- Data modeling dimensional modelling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design.
- Cloud data platforms: hands-on with AWS (Redshift, S3 Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow).
- Big data ecosystems: Spark, Hadoop, or Kafka for large-scale/streaming data.
- Data warehousing Snowflake, Redshift, BigQuery, or Databricks.
- ETL/ELT pipeline design: Airflow, dbt, Fivetran, or similar orchestration tools.
- Data governance & security: data lineage, access control, compliance (GDPR/SOC2), master data management.
Strongly Preferred:
- Experience architecting systems supporting ML/AI pipelines (feature stores, vector DBs, real-time inference data flows).
- Programming in Python or Scala for pipeline development.
- API/microservices architecture exposure, understanding how data systems integrate with application layers.
- Experience with data mesh/data lake house architectures.
- Prior experience presenting architecture decisions to leadership/stakeholders.
Nice-to-Have (Differentiators):
- Certifications: AWS/GCP/Azure data architecture certs.
- Experience in a high-growth startup (built systems from scratch, not just maintained legacy).
- Exposure to real-time/streaming architecture (Kafka, Kinesis, Flink).