Data Engineer

Evlo AI; Miami (FL); On-site; USD 110,000 - 160,000; Full time

Job summary

Evlo AI in Miami is seeking a data engineer to own the data infrastructure powering analytics and product decisions. You will design batch and streaming pipelines, warehouse modeling, and ensure end-to-end data reliability.

You will partner with data analysts, analytics engineers, and backend teams to turn raw event, transactional, and third-party data into trustworthy datasets used daily across the organization.

Qualifications

• 3–6 years of data engineering experience building and operating production pipelines.
• Expert-level SQL and strong Python skills, including distributed processing.
• Hands-on experience with a cloud data warehouse and orchestration tooling.
• Experience with dbt and modern data stack tooling for transformation, testing, and documentation.
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• Track record of translating business questions into data models with analysts.
• Bonus: Kafka/streaming architectures or building data platforms in startups.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines in Python and SQL, orchestrating workflows with Airflow or Dagster.
• Develop and optimize data models in Snowflake, including dimensional models and data contracts.
• Build and scale ingestion pipelines for streaming and batch sources using Kafka, Fivetran, or custom Python connectors.
• Implement data quality and observability frameworks to ensure trustworthy downstream analytics.
• Partner with analytics engineers to refine dbt models, semantic layers, and metric definitions used across BI tools.
• Write tested, production-grade code and contribute to IaC practices with Terraform.

Skills

Advanced SQL

Python programming

Spark

Airflow

Dagster

dbt

Snowflake

Data modeling

ETL/ELT pipelines

Terraform

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Airflow

Dagster

Prefect

Kafka

Fivetran

Snowflake

BigQuery

Redshift

Terraform

Job description

About The Role

The role owns the data infrastructure that powers analytics and product decisions across the business - batch and streaming pipelines, warehouse modeling, and the reliability of the data platform end-to-end.

You will partner closely with data analysts, analytics engineers, and backend teams to turn raw event, transactional, and third-party data into trustworthy, well-modeled datasets used daily across the organization.

Key Responsibilities

• Design, build, and maintain ETL/ELT pipelines in Python and SQL, orchestrating workflows with Airflow or Dagster
• Develop and optimize data models in Snowflake (or similar cloud warehouse), including dimensional models, incremental loads, and data contracts
• Build and scale ingestion pipelines for streaming and batch sources using Kafka, Fivetran, or custom Python connectors
• Implement data quality and observability frameworks - freshness checks, anomaly detection, and lineage tracking - to keep downstream analytics trustworthy
• Partner with analytics engineers to refine dbt models, semantic layers, and metric definitions used across BI tools
• Optimize warehouse performance and pipeline costs through query tuning, partitioning strategies, and incremental processingWrite tested, documented, production-grade code; contribute to code reviews and infrastructure-as-code practices with Terraform

What We Are Looking For

• 3–6 years of data engineering experience building and operating production pipelines at scale
• Expert-level SQL and strong Python skills, including experience with distributed processing (Spark or similar)
• Hands-on experience with a cloud data warehouse (Snowflake, BigQuery, or Redshift) and orchestration tooling (Airflow, Dagster, or Prefect)
• Experience with dbt and modern data stack tooling for transformation, testing, and documentation
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• Track record of collaborating with analysts and stakeholders to translate business questions into data models
• Bonus: experience with Kafka/streaming architectures, data catalog tooling, or building data platforms in fast-growing startups