Senior Data Engineer
Design, build, and maintain scalable data platforms that power analytics, machine learning, and business intelligence across the organization. You will develop reliable data pipelines, optimize data storage and processing, and ensure high-quality, accessible data for stakeholders. This role combines software engineering best practices with modern cloud-based data architecture to support both operational and analytical workloads.
Mission
Data is only valuable when it is accurate, reliable, and easily accessible. Your mission is to build and maintain scalable data infrastructure that enables teams to make informed decisions, automate business processes, and unlock insights through trusted, high-quality data.
Responsibilities
Build & Maintain Data Pipelines
Design, develop, and maintain robust ETL/ELT pipelines that ingest, transform, and deliver data from a variety of internal and external sources while ensuring reliability and scalability.
Design Scalable Data Architecture
Develop and optimize data warehouses, data lakes, and modern cloud-based storage solutions that support analytics, reporting, and machine learning workloads.
Ensure Data Quality & Reliability
Implement data validation, monitoring, testing, and observability practices to ensure data accuracy, consistency, availability, and timely delivery across systems.
Optimize Performance
Improve query performance, pipeline efficiency, and storage utilization by optimizing data models, partitioning strategies, indexing, and processing workflows.
Collaborate Across Teams
Partner with software engineers, analysts, data scientists, and business stakeholders to understand data requirements and deliver scalable data solutions that support organizational goals.
Maintain Data Governance
Support data governance initiatives by implementing security, access controls, documentation, metadata management, and compliance with organizational and regulatory standards.
Requirements
Data Engineering Portfolio
Experience designing and delivering production-grade data pipelines, scalable data platforms, or cloud-based data solutions. Demonstrated ability to build reliable, maintainable, and efficient data infrastructure.
Programming & Data Processing
Strong proficiency in Python and SQL, with experience developing ETL/ELT workflows and working with distributed data processing frameworks such as Apache Spark.
Cloud & Data Platforms
Experience with modern cloud platforms such as AWS, Azure, or Google Cloud, along with services for storage, orchestration, and large-scale data processing.
Data Warehousing
Hands-on experience with modern data warehouse technologies such as Snowflake, BigQuery, Amazon Redshift, Databricks, or Azure Synapse Analytics.
Orchestration & Automation
Experience building and maintaining automated workflows using orchestration tools such as Apache Airflow, Prefect, Dagster, or similar scheduling platforms.
Databases
Strong understanding of relational and NoSQL databases, data modeling, indexing strategies, and query optimization techniques.
Version Control & DevOps
Experience with Git, CI/CD pipelines, Infrastructure as Code, containerization (Docker), and deploying data workloads in production environments.
Data Quality & Monitoring
Knowledge of data testing, monitoring, observability, logging, and alerting practices to ensure reliable and trustworthy data pipelines.
Department
Engineering
Location
Remote