Data Engineer

We are seeking a skilled Data Engineer with expertise in Python, SQL, ETL, Airflow, Snowflake, and BigQuery to join a dynamic team remotely. In this role, you will design, build, and maintain scalable data pipelines and cloud data infrastructure that support analytics, reporting, and machine learning initiatives. Your work will ensure that data across the organization is accurate, reliable, and delivered on time to analysts, data scientists, engineering teams, and leadership. This hands-on position focuses on ETL/ELT development, data warehousing, workflow orchestration, data quality, and cloud infrastructure optimization.

Key Responsibilities

- Develop and maintain scalable ETL/ELT pipelines using Python and SQL, ingesting data from APIs, SaaS platforms, relational databases, cloud applications, and streaming systems.
- Build reusable connectors and components, troubleshoot pipeline failures, and resolve data inconsistencies.
- Manage workflow orchestration tools such as Apache Airflow, Prefect, or similar platforms to monitor pipeline health, schedule jobs, and implement automated retries and alerts.
- Design and optimize cloud data warehouses using Snowflake, BigQuery, or Redshift, including building analytics-ready data models and warehouse structures with star and snowflake schemas.
- Optimize SQL queries and warehouse workloads through partitioning, clustering, indexing, and efficient data modeling, while monitoring and controlling cloud costs.
- Implement automated data validation and quality checks using tools like dbt and Great Expectations, maintaining logging, lineage, and auditability across pipelines.
- Build and support streaming or event-driven data pipelines with technologies such as Kafka, Kinesis, or Pub/Sub, ensuring low-latency and reliable workflows.
- Containerize data services with Docker and support Kubernetes environments where applicable. Develop and maintain CI/CD workflows using GitHub Actions, Jenkins, or similar tools, and manage infrastructure-as-code with Terraform or CloudFormation.
- Collaborate closely with Data Analysts, Data Scientists, BI teams, Product, and Engineering to deliver curated datasets for dashboards, business intelligence, analytics, machine learning, and operational reporting.
- Maintain clear documentation for pipelines, schemas, workflows, and data definitions.

Required Qualifications

- Minimum of 3 years’ experience in Data Engineering or backend engineering roles.
- Strong proficiency in Python and SQL.
- Hands-on experience with at least one modern cloud data warehouse such as Snowflake, BigQuery, or Redshift.
- Proven experience building and maintaining ETL/ELT pipelines.
- Familiarity with orchestration platforms like Airflow or Prefect.
- Solid understanding of data modeling, warehouse architecture, and SQL query optimization.
- Knowledge of cloud-based data infrastructure and strong troubleshooting skills.
- Excellent written and verbal communication skills in English.

Preferred Qualifications and Benefits

- Experience with dbt and Great Expectations for data quality and validation.
- Familiarity with streaming technologies such as Kafka, Kinesis, or Pub/Sub.
- Knowledge of cloud data integration tools like AWS Glue, GCP Dataflow, or Azure Data Factory.
- Experience with Docker, Kubernetes, and infrastructure-as-code tools like Terraform or CloudFormation.
- Background in regulated industries such as healthcare or fintech is a plus.
- Experience optimizing large-scale warehouse performance and managing cloud costs.
- Exposure to Scala and CI/CD pipeline development.

This role offers the opportunity to work with modern cloud-native data infrastructure, solve complex scalability and reliability challenges, and collaborate across engineering, analytics, and data science teams. You will have high technical ownership in a fully remote environment with strong career growth potential into senior and specialized data engineering roles.

Typical daily activities include reviewing pipeline health, building new data connectors, optimizing workflows and SQL queries, troubleshooting data quality issues, monitoring warehouse usage, documenting data assets, and collaborating on infrastructure improvements.

Success in this role is measured by maintaining pipeline uptime above 99%, ensuring data freshness within SLAs, preventing critical data quality issues, improving query and warehouse performance, reducing costs, and delivering reliable, scalable ETL infrastructure with strong monitoring and rapid failure resolution.

If you are passionate about building robust, scalable data systems and ensuring data accuracy and reliability, this position offers a rewarding opportunity to make a significant impact on business decision-making through data.