Data Platform Engineer

Open Systems Technologies; Cincinnati (OH); On-site; USD 140,000 - 210,000; Full time

Job summary

Open Systems Technologies seeks a Data Engineer with 7+ years of experience to own multi-stage data pipelines that ingest from external sources into a governed lake/warehouse. You will deliver scaled, event-driven integrations and manage ETL for downstream services and API access for billions of data points.

Responsibilities include building ingestion connectors, implementing idempotent jobs, and standing up observability with SLOs and latency dashboards.

Qualifications

• 7–10+ years backend/data engineering with production ownership of large event-driven data systems.
• Proficient in Python, Node, and similar toolsets for ETL job implementation; strong testing and reliability toolset.
• Deep AWS experience and Terraform-based IaC; CI/CD for data and application deployments.
• Expert SQL and performance tuning on Redshift, Snowflake, or equivalent.
• Experience delivering idempotent pipelines, restaging/reconciliation, and parity validation against legacy systems.
• Experience in highly-governed environments (HIPAA, GLBA, PCI, etc.).
• Solid security and compliance practices, supporting internal and external audits (SOC 2 ISO 27001, etc.) and remediation.

Responsibilities

• Design and implement ingestion/connectors (BigQuery direct, CSV→JSON, REST) and normalization into standardized data models.
• Build event-driven jobs and services (Python/Node) that enrich, dedupe, and apply rules; ensure idempotency and safe replays.
• Define data contracts, schema evolution, backfill strategy, and cutover plans; partner with stakeholders on acceptance criteria.
• Operate AWS workloads (EC2, Lambda, AppRunner, RDS, Redshift) with Terraform; secure secrets, roles, and least-privilege access.
• Optimize SQL for MPP systems (Redshift, Snowflake, or similar); profile queries, partition/cluster, and tune materializations.
• Implement observability (logs, metrics, tracing, lineage) and incident response; drive postmortems and remediation.
• Maintain concise documentation of architecture, workflows, standards, and governance.

Skills

Python

Node.js

ETL pipelines

SQL

Tools

AWS

Terraform

Redshift

Snowflake

Job description

Our healthcare client is looking for a Data Engineer with 7+ years experience to own the multi-stage data pipeline layer that ingests from external sources (BigQuery direct queries, vendor file feeds, APIs) into a governed lake/warehouse. Deliver scaled event-driven integrations; managing ETL for downstream integrations, an event-bus for user engagement services, and API / query access for billions of data points.

What you'll do in this role:

Ship a production-ready, like-for-like ETL replacement with zero data loss and validated parity against legacy outputs.

Establish lake → warehouse pipelines with schemas, contracts, and idempotent restatement for corrections.

Implement rules evaluation and event context enrichment to support downstream filtering and event-response integrations.

Stand up observability for pipelines and jobs (SLOs, error/retry rates, latency dashboards, lineage) and on-call runbooks.

Provision reproducible infrastructure with Terraform; define CI/CD and cost/performance guardrails across environments.

Develop and support agentic workflows (data access, context management, & modern AI development practices in your own work) across the organization.

Key Responsibilities

Design and implement ingestion/connectors (BigQuery direct, CSV→JSON, REST) and normalization into standardized data models.

Build event-driven jobs and services (Python/Node) that enrich, dedupe, and apply rules; ensure idempotency and safe replays.

Define data contracts, schema evolution, backfill strategy, and cutover plans; partner with stakeholders on acceptance criteria.

Operate AWS workloads (EC2, Lambda, AppRunner, RDS, Redshift) with Terraform; secure secrets, roles, and least-privilege access.

Optimize SQL for MPP systems (Redshift, Snowflake, or similar); profile queries, partition/cluster, and tune materializations.

Implement observability (logs, metrics, tracing, lineage) and incident response; drive postmortems and remediation.

Maintain concise documentation of architecture, workflows, standards, and governance.

Qualifications

7–10+ years backend/data engineering with production ownership of large event-driven data systems.

Proficient in Python, Node, and similar toolsets for ETL job implementation; strong testing and reliability toolset.

Deep AWS experience and Terraform-based IaC; CI/CD for data and application deployments.

Expert SQL and performance tuning on Redshift, Snowflake, or equivalent.

Experience delivering idempotent pipelines, restaging/reconciliation, and parity validation against legacy systems.

Experience in highly-governed environments (HIPAA, GLBA, PCI, etc.).

Solid security and compliance practices, supporting internal and external audits (SOC 2 ISO 27001, etc.) and remediation.

Preferred

Orchestration experience (Airflow or similar), streaming (Kinesis/Kafka/SQS), and dbt or warehouse-centric modeling.

Data quality frameworks, lineage/metadata tooling, and SLA/SLO design.

Exposure to real-time enrichment and rules engines; familiarity with warehouse-native features (tasks/streams).

Things you should be able to do:

Diagram a multi-stage lake → warehouse pipeline with cutover, backfill, restaging for corrections, and SLOs.

Discuss agentic workflow support from a data/platform perspective.

Demonstrate experience (positive and lessons-learned moments) for at-scale data/platform implementations.