Staff Data Platform Engineer
New
Australia, Canada, India, Ireland, New Zealand, Singapore, South Africa, United Kingdom, United StatesFull-TimeStaff
SalaryUSD 194000 - 220000 / year
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Job Details
- Experience
- 8–10+ years
- Required Skills
- AWSPythonETLAirflowdbtDatadog
Requirements
- 8–10+ years of experience building and operating large-scale data platforms, pipelines, and warehouses in cloud environments.
- Deep expertise in AWS.
- Strong software engineering fundamentals and experience building production-grade systems.
- Proven track record leading complex technical initiatives and influencing architecture across teams.
- Deep expertise in modern data warehousing, ELT/ETL architectures, and data platform design.
- Experience operating data platforms with a focus on reliability, observability, data quality, and cost optimization.
- Proven ability to leverage modern AI-assisted and agentic development tools to architect, build, and operate production-grade systems.
- Experience partnering with data scientists, analysts, product managers, and software engineers to deliver business impact.
- Demonstrated ability to navigate ambiguity, drive alignment, and lead through influence rather than authority.
- Hands-on experience with Python, Amazon Redshift, AWS-native ETL technologies (ZeroETL, DMS), Airflow, dbt, Datadog, Fivetran, and AWS CDK preferred.
- Experience designing data governance, metadata, lineage, or self-service data platforms preferred.
- Experience supporting AI, machine learning, or agentic workflow initiatives preferred.
Responsibilities
- Lead the architecture and development of scalable, secure, and cost-efficient data platforms on AWS.
- Architect and optimize the Data Warehouse to support analytical, operational, and AI-driven workloads.
- Define and implement Infrastructure-as-Code standards using AWS CDK.
- Design and operate scalable orchestration frameworks using Airflow and Fivetran.
- Partner with the Data Analytics team to establish data modeling standards and transformation practices using dbt.
- Establish governance, metadata, lineage, and access patterns to make data discoverable and trustworthy.
- Define how employees and AI-powered systems access and leverage company data.
- Establish monitoring, alerting, and observability standards using Datadog.
- Provide technical leadership, architectural guidance, and mentorship across the data organization.
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