Senior Data Engineer

Location: Remote

Company

hirify.global delivers data engineering engagements for customers, including production data platforms and pipelines.

What you will do

• Build and operate robust batch and streaming ingestion pipelines from databases, event streams, and third-party APIs.
• Own storage architecture across data lakes, lakehouses, and warehouses, optimizing partitioning, cost efficiency, and query performance.
• Implement orchestration, automated testing, alerting, observability, restartability, and clean recovery for production pipelines.
• Apply infrastructure as code, CI/CD for data workloads, code reviews, and strict version control.
• Establish data contracts with data-producing teams and manage schema drift, access, retention, lineage, and documentation.
• Work directly with leadership and customer teams while guiding others and communicating clearly.

Requirements

• Advanced, production-grade SQL and Python.
• Production depth with one major cloud provider: AWS, GCP, or Azure, plus transferable cross-cloud principles.
• Production experience with a major-vendor orchestrator such as Airflow, Dagster, Azure Data Factory, or equivalent.
• Strong warehouse experience with Snowflake or BigQuery, including dimensional or Data Vault modelling, and dbt or an equivalent transformation framework.
• Extensive experience with Snowflake and Terraform.
• Proven experience building and running production data pipelines, including the ability to explain lessons learned from failures.
• Fluent English required, with confidence working directly with customer teams.
• Daily use of AI tooling with strict review and verification before generated output is shipped.

Nice to have

• Cost engineering experience, including quantifying pipeline savings.
• Exposure to regulated environments, GDPR obligations, access control, and auditable lineage.
• Scaled Spark or PySpark, Databricks, and streaming or pub/sub brokers such as Kafka, Kinesis, Pub/Sub, or MQTT-class brokers.
• AI data infrastructure including embedding pipelines, vector stores, retrieval feeds, feature stores, and Parquet, Iceberg, or Delta formats.
• Docker, Kubernetes, GitHub Actions, and production data quality frameworks such as Great Expectations, Soda, or Monte Carlo.
• NoSQL stores such as MongoDB and marketing or advertising platform APIs, including Google Ads, Meta, or CRM systems.

Culture & Benefits

• Remote work.
• Collaborative work with company leadership, customer teams, and other engineers.
• Emphasis on continuous learning, clear communication, software engineering discipline, and durable documentation.