Senior Data Engineer

About DevSavant

DevSavant is an operating partner for startups and growth-stage companies, helping them turn ambition into execution.

We support founders and leadership teams with product engineering and global staffing, from early prototypes and MVPs to scaling high-performing teams. Our vetted talent across LATAM and Asia embeds directly into client teams, operating as true extensions rather than external vendors.

With over 8 years working in venture-backed ecosystems, DevSavant is trusted to accelerate delivery, scale teams efficiently, and support companies as they reach their next milestone.

About the Role

We are seeking a Senior Data Engineer to join a cross-functional team working on scalable data systems and analytics infrastructure.

This is an individual contributor role focused on building, maintaining, and optimizing data pipelines and data models that power analytics and business-critical decision-making. The role requires a strong technical generalist mindset, combining software engineering principles with deep data expertise.

You will work closely with Data Science, Data Ops, and business stakeholders to ensure data is accurate, accessible, and structured for self-service analytics. The ideal candidate is someone who enjoys working with complex datasets, simplifying systems, and building scalable data infrastructure from the ground up.

Key Responsibilities

Data Engineering & Pipeline Development

Own, build, maintain, and optimize scalable data pipelines

Design and implement data architectures that support analytics and operational use cases

Work with large, complex datasets to meet evolving business requirements

Ensure data quality, reliability, and performance across systems

Apply best practices for developing specialized datasets for analytics and modeling

Continuously improve data workflows, pipelines, and infrastructure

Data Modeling & Analytics Enablement

Develop a deep understanding of core data models and business logic

Partner with Data Science and Data Ops teams to maintain trusted, well-documented datasets

Enable self-service analytics by structuring and organizing data effectively

Support analytical workflows and downstream consumption of data

Assist analysts with query development and dataset preparation

Cross-functional Collaboration

Work with a wide range of stakeholders to gather requirements and translate them into technical solutions

Communicate complex technical concepts clearly to both technical and non-technical audiences

Collaborate closely with engineering, analytics, and product teams

Contribute to documentation and knowledge sharing across teams

Infrastructure & Systems Design

Contribute to the design of scalable and maintainable systems

Optimize data delivery and infrastructure for performance and scalability

Support integration across multiple data platforms and tools

Maintain and improve existing systems, including search and indexing solutions

Debugging, Optimization & Reliability

Independently troubleshoot complex systems and resolve data-related issues

Perform root cause analysis and implement long-term fixes

Improve system reliability and performance through monitoring and optimization

Ensure stability and efficiency of data platforms

Core Technical Stack

Data & Backend

SQL for querying, transformation, and data modeling

Python or other general-purpose programming languages (e.g., JavaScript/TypeScript, Java, C#, Go, Scala)

Experience with data pipeline tools such as Spark and DBT

Data warehouses such as BigQuery, Snowflake, or Databricks

Data Orchestration & Processing

Workflow orchestration tools such as Airflow or Dagster

Experience handling large-scale data processing and transformations

Familiarity with batch and/or streaming data systems

Infrastructure & Cloud

Cloud platforms such as GCP or AWS

Infrastructure as Code tools (Terraform, Pulumi, or CloudFormation)

Experience designing scalable and maintainable systems

Additional Tools & Systems

Experience with backend engineering and web services is a plus

Familiarity with analytics and data visualization ecosystems

Exposure to transaction, receipt, or viewership data is beneficial

Required Qualifications

5+ years of experience in software engineering, with at least 3 years focused on data engineering or data infrastructure

Strong expertise in SQL and working with relational databases

Experience building and maintaining scalable data pipelines

Proficiency in at least one general-purpose programming language (Python preferred)

Experience with modern data stack tools (e.g., Spark, DBT, Airflow/Dagster)

Strong debugging and problem-solving skills in complex systems

Experience working with cloud data warehouses (BigQuery, Snowflake, or Databricks)

Ability to design data systems that support analytics and business intelligence

Strong communication skills and ability to work cross-functionally

Experience documenting and simplifying complex systems

Nice to Have

Experience with backend engineering and API development

Experience with Infrastructure as Code (IaC) tools

Exposure to system design for customer-facing or high-scale platforms

Familiarity with analytics-heavy environments and data-driven products

Experience working with large-scale, real-world datasets (e.g., transactions, behavioral data)

Qualities We're Looking For

Ownership mindset: Ability to take responsibility and drive systems end-to-end

Technical versatility: Strong foundation as a software engineer with data expertise

Problem-solving focus: Ability to navigate ambiguity and solve complex challenges

Communication skills: Clear and effective collaboration across teams

Execution-driven: Ability to move quickly and deliver results in a fast-paced environment

Continuous improvement: Desire to refine systems, processes, and technical approaches