Member of Technical Staff, Data Engineering
M
micro1Data infrastructure
Source API remote eligibility restrictions: United StatesFull-TimeStaff
SalaryBase salary of $200,000 –$260,000
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Job Details
- Required Skills
- PythonSQLSnowflakeAirflowData engineeringData modelingdbt
Requirements
- Bring Staff-level engineering expertise and extensive hands-on experience building and operating production data platforms.
- Have personally led the design and implementation of a data platform from scratch.
- Demonstrate deep understanding of data warehouses, ETL/ELT architectures, data modeling, orchestration, and distributed data processing.
- Have strong proficiency in SQL and Python and software engineering fundamentals for reliable data infrastructure.
- Have experience with modern data technologies such as Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, Dagster, or equivalent tools.
- Understand data quality, lineage, observability, access controls, and production data reliability.
- Have experience designing analytics infrastructure for product insights and operational decision-making.
- Use AI coding agents in daily work and automate repetitive development and data engineering tasks.
- Make pragmatic architectural decisions that balance scalability, simplicity, performance, and engineering velocity.
- Demonstrate technical leadership, including mentoring engineers and establishing engineering standards.
Responsibilities
- Architect and implement the internal data warehouse, ingestion infrastructure, ETL/ELT pipelines, and core data models.
- Design reliable, documented datasets and interfaces that AI agents can discover, query, and use.
- Transform fragmented operational and product data into consistent, high-quality sources of truth.
- Build infrastructure for engineering, product, and business teams to answer questions independently.
- Establish event tracking, instrumentation, data models, and reporting foundations for product analytics.
- Use AI coding agents and automation to accelerate pipeline development, validation, documentation, monitoring, and troubleshooting.
- Establish data quality standards, observability, governance, and scalable architectural patterns.
- Mentor engineers, establish data engineering practices, and lead architectural decisions through hands-on execution.
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