Senior Data Engineer
New
J
JobgetherFintech
USFull-TimeSenior
SalaryCompetitive base salary range of $171,000–$207,000 USD
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonSnowflakeAirflowData engineeringTerraformdbt
Requirements
- 5+ years of experience as a Data Engineer focused on production data pipelines and data infrastructure.
- Strong hands-on experience with AWS data services, including cloud storage, compute, and pipeline development.
- Experience managing cloud data warehouses such as Snowflake or equivalent platforms, including access management, performance optimization, and cost control.
- Advanced Python skills with the ability to independently develop and maintain complex production systems.
- Experience with workflow orchestration tools such as Airflow or similar technologies.
- Familiarity with data governance, observability, monitoring, and data quality frameworks.
- Understanding of event sourcing, change data capture, or similar data architecture patterns.
- Strong interest in leveraging AI tools and workflow automation to improve engineering efficiency.
- Experience with data lakehouse architectures and open table formats such as Apache Iceberg or Delta Lake is a plus.
- Experience with dbt-core in production environments is preferred.
- Experience building infrastructure for AI-driven data access, governance frameworks, or AI data quality evaluation is a plus.
- Background in fintech, financial services, or other regulated industries is beneficial.
Responsibilities
- Define and maintain data governance standards, including access controls, data lineage, data contracts, and best practices for managing production data assets.
- Lead the technical evolution of the data platform toward an AI-ready architecture that supports advanced data consumption and emerging AI capabilities.
- Design, develop, and maintain scalable data pipelines across ingestion, orchestration, transformation, and delivery workflows.
- Build reliable data infrastructure using technologies such as AWS, Terraform, Airflow, Snowflake, dbt, Meltano, and Python.
- Improve platform performance, reliability, and data quality through monitoring, optimization, and SLA-based prioritization.
- Create solutions that make data accessible and valuable for analysts, business teams, applications, and AI systems.
- Champion the adoption of AI-assisted development tools and automation practices to improve engineering productivity.
- Mentor data engineers and analysts by sharing technical knowledge and helping raise engineering standards.
- Collaborate closely with product, finance, analytics, and engineering teams to ensure data solutions meet evolving business needs.
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