Data Engineer

Evlo AI; Raleigh (NC); On-site; USD 110,000 - 170,000; Full time

Job summary

Evlo AI is seeking a data engineer who owns the data infrastructure powering analytics and product decisions. You will design ELT pipelines, ingest data from Postgres, Kafka, and third‑party APIs into a cloud warehouse, and ensure hundreds of daily jobs run reliably.

You will model data with dbt, manage orchestration in Airflow or Dagster, and build streaming pipelines for low-latency event data. Strong SQL, Python, and cloud warehouse experience are required.

Qualifications

• 3-6 years of data engineering experience building and operating production pipelines at meaningful scale.
• Strong SQL and Python skills; deep experience with at least one cloud data warehouse (Snowflake/BigQuery/Redshift).
• Hands-on production experience with dbt and an orchestrator (Airflow, Dagster, or Prefect).
• Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tooling (Terraform preferred).
• Solid grounding in data modeling — dimensional modeling, slowly changing dimensions, incremental processing — and distributed processing (Spark a plus).
• Bachelor's degree in CS/Engineering or equivalent practical experience.
• Bonus: Streaming architectures (Kafka/Flink) or data platforms serving many internal consumers.

Responsibilities

• Design, build, and maintain ELT pipelines in Python and SQL ingesting from Postgres, Kafka, and third-party APIs into a cloud warehouse.
• Develop and enforce dimensional and wide-table data models using dbt, including testing, documentation, and lineage tracking.
• Own orchestration and scheduling in Airflow or Dagster, including DAG design, dependency management, and failure recovery.
• Build streaming and near-real-time pipelines for event data using Kafka, Kinesis, or Flink where latency matters.
• Implement data quality frameworks—freshness checks, schema validation, anomaly detection—with automated alerting.
• Optimize warehouse performance and cost: partitioning, clustering, query tuning, and incremental materialization.
• Partner with analysts and product teams to define contracts for core datasets and reduce ad hoc pipeline firefighting.

Skills

Advanced SQL

Python

Data modeling fundamentals

Cloud data warehouses

Streaming data processing

Analytics collaboration

Education

Bachelor's degree in CS/Engineering or related field

Tools

dbt

Airflow

Dagster

Prefect

Snowflake

BigQuery

Redshift

Terraform

Kafka

Kinesis

Flink

Job description

About The Role The role owns the data infrastructure that powers analytics and product decisions — batch and streaming pipelines, warehouse modeling, and the orchestration layer that keeps hundreds of daily data jobs running reliably.

About The Role The role owns the data infrastructure that powers analytics and product decisions — batch and streaming pipelines, warehouse modeling, and the orchestration layer that keeps hundreds of daily data jobs running reliably.
You will work directly with analytics engineers, data scientists, and backend teams to turn raw event, transactional, and third-party data into modeled, trustworthy datasets that downstream teams build on without hesitation.
Key Responsibilities

• Design, build, and maintain ELT pipelines in Python and SQL, ingesting from sources like Postgres, Kafka, and third-party APIs into a cloud warehouse (Snowflake, BigQuery, or Redshift)
• Develop and enforce dimensional and wide-table data models using dbt, including testing, documentation, and lineage tracking
• Own orchestration and scheduling in Airflow or Dagster, including DAG design, dependency management, and failure recovery
• Build streaming and near-real-time pipelines for event data using Kafka, Kinesis, or Flink where latency matters
• Implement data quality frameworks — freshness checks, schema validation, anomaly detection — with automated alerting to on-call rotations
• Optimize warehouse performance and cost: partitioning strategies, clustering, query tuning, and incremental materialization patterns
• Partner with analysts and product teams to define contracts for core datasets and reduce ad hoc pipeline firefighting
  

What We Are Looking For

• 3-6 years of experience in data engineering, building and operating production pipelines at meaningful scale
• Advanced SQL and strong Python; deep experience with at least one cloud data warehouse (Snowflake, BigQuery, or Redshift)
• Hands-on production experience with dbt and an orchestrator such as Airflow, Dagster, or Prefect
• Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tooling (Terraform preferred)
• Solid grounding in data modeling fundamentals — dimensional modeling, slowly changing dimensions, incremental processing — and distributed data processing (Spark a plus)
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Bonus: Experience with streaming architectures (Kafka/Flink), data lakehouse formats (Iceberg, Delta Lake), or building data platforms serving 50+ internal consumers