Data Engineer

At TigerData, formerly Timescale, we empower developers and businesses with the fastest PostgreSQL platform designed for transactional, analytical, and agentic workloads. Trusted globally by thousands of organizations, TigerData accelerates real-time insights, drives intelligent applications, and powers critical infrastructure at scale. As a globally distributed, remote-first team committed to direct communication, accountability, and collaborative excellence, we're shaping the future of data infrastructure, built for speed, flexibility, and simplicity.

TigerData is looking for a skilled and innovative Data Engineer with expertise in building scalable data infrastructure and a passion for enabling data-driven decision making across the organization. You will play a crucial role in designing, building, and maintaining the data systems that power our analytics, product insights, and business intelligence initiatives for Timescale, our open-source database for real-time analytics and time series at scale.

Data Engineers at Timescale are essential for ensuring our teams have reliable, accurate, and accessible data to make informed decisions. You'll design and implement robust ETL/ELT processes, manage data infrastructure, optimize database performance, and collaborate closely with Product, Finance, Engineering, and Leadership teams to enable self-service analytics and data democratization.

You'll succeed at Timescale if you are systematic, detail-oriented, performance-focused, a collaborative problem-solver, excited by technical challenges and scale, and passionate about building reliable data infrastructure that empowers teams to extract insights from complex datasets.

Timescale is a remote company with team members around the world, and English language fluency is a requirement. The preferred candidate for this role will be based in the United States or Europe.

Responsibilities:

• Design, build, and maintain scalable data pipelines and ETL/ELT processes to ingest, transform, and deliver data from various sources including application databases, event streams, and third-party APIs.

• Architect and optimize data warehouse solutions, ensuring efficient storage, retrieval, and processing of large-scale time-series and analytical datasets.

• Implement and maintain data quality frameworks, monitoring systems, and alerting mechanisms to ensure data accuracy, completeness, and reliability across all data systems.

• Collaborate with Product Managers, Marketing, Finance, and Sales to understand data requirements and build infrastructure that enables self-service analytics and advanced data exploration.

• Optimize database performance, including query optimization, indexing strategies, and capacity planning for both operational and analytical workloads.

• Build and maintain data infrastructure using cloud platforms (AWS, GCP, Azure) and modern data stack tools, ensuring scalability, security, and cost-effectiveness.

• Develop and maintain data documentation, schemas, and governance processes to ensure data discoverability and proper usage across teams.

• Work closely with Engineering teams to implement event tracking, logging, and instrumentation that captures meaningful product and user behavior data.

• Support real-time data processing requirements and streaming analytics use cases, leveraging Timescale's time-series capabilities.

• Champion data engineering best practices, including version control, testing, monitoring, and CI/CD for data pipelines.

Requirements:

• 4+ years of proven experience as a Data Engineer, Analytics Engineer, or similar role, with significant experience building and maintaining production data pipelines.

• Expert proficiency in SQL for complex data transformations, performance optimization, and working with large datasets. Strong PostgreSQL experience is highly preferred.

• Proficiency in Python or another programming language for data pipeline development, automation, and scripting.

• Experience with modern data stack tools such as dbt, Airflow, Dagster, or similar orchestration and transformation frameworks.

• Strong experience with cloud data platforms (AWS Redshift/RDS, Google BigQuery/Cloud SQL, Azure Synapse, or Snowflake) and their associated data services.

• Understanding of data modeling concepts, dimensional modeling, and database design principles for both OLTP and OLAP systems.

• Experience with data visualization and BI tools (Metabase, Tableau, Looker) and building data marts for analytical consumption.

• Strong understanding of data governance, security, and privacy principles, including experience with data lineage and cataloging tools.