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Data Architect
Job Description
We're seeking a highly experienced Principal Data Engineer to drive the architecture, strategy, and delivery of data pipelines across our Enterprise Data platform. This role combines hands-on engineering, technical leadership, and data governance strategy.
You're passionate about building reliable, scalable data pipelines with Python, Airflow, and dbt Core—and about setting the standards that govern our data across its full life cycle. You'll partner closely with Product and Architecture leadership to set technical direction while mentoring engineers across the team.
Lead the architecture and strategy for ETL/ELT pipelines using Python, Airflow, and dbt Core across the Enterprise Data platform.
Define and drive data management standards across the full data life cycle ingestion, transformation, quality, governance, retention, and archival.
Partner with Product, Architecture, and business stakeholders on data strategy and roadmap planning.
Mentor engineers across multiple experience levels and set engineering best practices for pipeline development.
Drive adoption of cloud-native, GCP-based orchestration such as Cloud Composer, and storage such as GCS.
Own data governance, cataloging, and lineage strategy.
Lead root-cause analysis and performance optimization for large-scale data pipelines.
Drive CI/CD automation and observability for data pipelines.
Required Qualifications
8+ years of experience in data engineering, with leadership responsibility.
Deep expertise in Python,Apache Airflow,ETL/ELT architecture at enterprise scale,dbt Core
Deep understanding of data management principles and the full data life cycle.
Proven experience architecting large-scale data pipelines and data platforms. Strong SQL and data warehousing expertise.
Experience with cloud-native data platforms, with a preference for Google Cloud Platform (GCP); other cloud platforms are also acceptable.
Experience mentoring engineers and leading technical initiatives.
Strong communication skills for partnering with stakeholders across the business.
Deep experience with GCP, including Google Cloud Storage (GCS) and Cloud Composer.
Experience With Kafka Or Other Event Streaming Platforms.
Experience with data governance and cataloging tools. Experience with CI/CD and observability tooling for data pipelines.
You're passionate about building reliable, scalable data pipelines with Python, Airflow, and dbt Core—and about setting the standards that govern our data across its full life cycle. You'll partner closely with Product and Architecture leadership to set technical direction while mentoring engineers across the team.
Lead the architecture and strategy for ETL/ELT pipelines using Python, Airflow, and dbt Core across the Enterprise Data platform.
Define and drive data management standards across the full data life cycle ingestion, transformation, quality, governance, retention, and archival.
Partner with Product, Architecture, and business stakeholders on data strategy and roadmap planning.
Mentor engineers across multiple experience levels and set engineering best practices for pipeline development.
Drive adoption of cloud-native, GCP-based orchestration such as Cloud Composer, and storage such as GCS.
Own data governance, cataloging, and lineage strategy.
Lead root-cause analysis and performance optimization for large-scale data pipelines.
Drive CI/CD automation and observability for data pipelines.
Required Qualifications
8+ years of experience in data engineering, with leadership responsibility.
Deep expertise in Python,Apache Airflow,ETL/ELT architecture at enterprise scale,dbt Core
Deep understanding of data management principles and the full data life cycle.
Proven experience architecting large-scale data pipelines and data platforms. Strong SQL and data warehousing expertise.
Experience with cloud-native data platforms, with a preference for Google Cloud Platform (GCP); other cloud platforms are also acceptable.
Experience mentoring engineers and leading technical initiatives.
Strong communication skills for partnering with stakeholders across the business.
Deep experience with GCP, including Google Cloud Storage (GCS) and Cloud Composer.
Experience With Kafka Or Other Event Streaming Platforms.
Experience with data governance and cataloging tools. Experience with CI/CD and observability tooling for data pipelines.
More Info
Key Skills
dbt Core
Cloud Composer
cataloging tools
observability tooling
About Company
Skills:
Data Quality, Azure Databricks, Data Governance, Sql, ELT, Azure Synapse Analytics, Data Modelling, Python, Etl, cdc, Infrastructure as Code, Migration architecture, Azure cloud architecture, ADLS Gen2, CI CD, Unity Catalog, SCD, Azure Data Platform, Delta Lake, Lakehouse architecture, Data pipelines
Skills:
data engineering , data warehouses , Metadata Management, Data Integration, Data Architecture, Microsoft Azure, Data Governance, Aml, Data Lineage, Apis, Data Management, Data Quality, Client Lifecycle Management, Analytics Platforms, Data Sovereignty, Cloud Platforms, Data Residency, Kyc, Cross-Border Data Transfer, Stakeholder Management, Data Lakes, Physical Data Models, Logical Data Models, Enterprise Data Models
Skills:
data sharing , snowflake , S3, Sql, Kinesis, Iam, Python, AWS, Performance Tuning, Kafka, Data Modeling, Step Functions, Dms, Snowpark, dbt, Glue, lakehouse patterns, cost governance, rbac, dimensional data vault
Skills:
snowflake , Apis, Data Architecture, Data Modeling, Sql, Python, cloud platforms, data visualization tools, data integration tools, dbt, CI CD pipelines, data architecture concepts
Skills:
Power Bi, Neo4j, PostgreSQL, SQL Server, Spark, Databricks, PBAC, Graph Data Science, Delta Lake, ABAC, Cypher