Data Engineer

We are seeking a strategic and technically skilled Data Engineer to join our data and engineering team. The ideal candidate will design, build, and maintain scalable data pipelines and platforms that enable analytics, machine learning, and business intelligence across the organization. This role requires strong software engineering practices, experience with cloud data architectures, ETL/ELT development, data modeling, and collaboration with product, analytics, and operations teams to deliver reliable, high-performance data solutions.

Key Responsibilities for Data Engineer - Pipeline Development, Platform & Reliability

• Design, develop, and operate robust ETL/ELT pipelines to ingests, transform, and deliver data from a variety of sources to data warehouses, data lakes, and downstream consumers.

• Build and maintain scalable data platforms and services in cloud environments (e.g., AWS, GCP, Azure), leveraging managed services and infrastructure as code for repeatable deployments.

• Implement and enforce data modeling standards, schema design, and metadata management to support analytics, BI, and ML use cases; collaborate with data analysts and scientists on schema requirements.

• Optimize data storage and query performance through partitioning, indexing, materialized views, and query tuning to meet latency and cost objectives.

• Develop automated data quality checks, monitoring, observability, and alerting to ensure data reliability and to quickly detect and remediate issues.

• Collaborate with product, engineering, and analytics partners to prioritize data work, support feature launches, and integrate data requirements into development lifecycles.

• Apply software engineering best practices—including testing, CI/CD, code reviews, and documentation—to data pipelines, transformations, and platform components.

• Support data governance, security, and access controls by implementing role-based access, encryption, and compliance-minded practices in collaboration with security and privacy teams.

• Identify opportunities to automate manual processes, reduce technical debt, and improve performance and cost-efficiency of data infrastructure.

• Mentor junior engineers, contribute to team processes and playbooks, and present technical designs and project progress to stakeholders as needed.

Required Qualifications - Data Engineer Skills & Experience

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field, or equivalent practical experience.

• 3+ years of hands-on experience building and operating data pipelines, ETL/ELT processes, or data platform components in a production environment.

• Proficiency with one or more programming languages used for data engineering (e.g., Python, Scala, Java) and experience with workflow/orchestration tools (e.g., Airflow, Prefect, dbt).

• Experience with cloud data services and storage technologies (e.g., Amazon Redshift, Snowflake, BigQuery, S3, GCS) and familiarity with cloud compute services (e.g., EMR, Dataproc, Glue, Kubernetes).

• Strong SQL skills and experience designing data models for analytics and reporting; understanding of normalization, dimensional modeling, and star/snowflake schemas.

• Familiarity with data quality frameworks, monitoring, logging, and observability practices; experience implementing automated tests for data pipelines.

• Knowledge of data security, privacy principles, and access control mechanisms; experience working with PII-sensitive data and compliance controls is a plus.

• Excellent problem-solving, communication, and collaboration skills; ability to translate business requirements into scalable technical solutions.

Preferred Qualifications - Tools, Certifications & Domain Experience

• Experience with modern data engineering tooling such as dbt, Kafka, Spark, Flink, or similar streaming/batch frameworks.

• Hands-on experience with infrastructure-as-code (e.g., Terraform, CloudFormation), containerization, and CI/CD for data workloads.

• Familiarity with BI tools (e.g., Looker, Tableau, Power BI) and experience enabling self-service analytics for business users.

• Advanced degree in a technical discipline, relevant certifications, or prior experience in a fast-growing SaaS or data-driven organization is a plus.

Work Environment & Compensation - Data Engineer Salary & Benefits

• Full-time position with an onsite work model.

• Competitive salary commensurate with experience and a comprehensive benefits package, including health insurance, retirement plan options, and paid time off.

• Opportunities for professional development, training, and support for certifications; clear paths for career growth within a collaborative and inclusive team environment.

• Culture that values diversity, equity, and inclusion, work-life balance, and employee well-being.