Staff Data Engineer
New
C
ClariumHealthcare supply chain
Location: Remote/USFull-TimeStaff
SalaryTarget Base Salary Range: $170K - $210K
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Job Details
- Experience
- 8+ years of data or software engineering experience
- Required Skills
- AWSPostgreSQLPythonSQLSnowflakeAirflowApache KafkaData modelingdbt
Requirements
- Bring 8+ years of data or software engineering experience, including significant time owning production systems end to end.
- Demonstrate expert-level SQL, including complex analytical queries and reading query plans to diagnose performance.
- Use Python for production data work, including pipeline code, transformation logic, testing, and tooling.
- Have deep relational database experience, including schema design, normalization tradeoffs, transactions, and performance tuning; Postgres and Snowflake are strongly preferred.
- Bring hands-on experience with data pipeline and orchestration tools such as Airflow, Dagster, Prefect, dbt, Fivetran, Spark, or Kafka.
- Have production experience running data workloads on AWS, such as S3, RDS, Lambda, or ECS, and understand related cost, security, and networking implications.
- Show a track record of leading multi-quarter, cross-team initiatives involving teams you do not manage.
- Be comfortable deciding what to build amid ambiguity.
Responsibilities
- Own the architecture and technical roadmap for core data infrastructure on AWS, spanning ingestion, transformation, storage, and serving.
- Design, build, and operate reliable batch and near-real-time pipelines with clear SLAs and observability.
- Model supply chain data from external ERP systems into a reusable warehouse model and lead migration of legacy assets.
- Tune performance and cost across Postgres and Snowflake, including query plans, indexing, partitioning, warehouse sizing, and storage strategy.
- Establish standards for data testing, code review, CI/CD, data quality checks, and documentation.
- Mentor engineers through design reviews, pairing, and technical feedback.
- Partner with analytics, product, and engineering teams to turn ambiguous questions into durable data models.
- Lead governance for sensitive data, including lineage, access controls, retention, auditability, and de-identification where required.
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