Senior Data & Platform Engineer – Veterinary Technology

Remote from: LATAM
Annual salary: Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department: Software Engineering
Employment type: Full Time,
Job posted: 12 Jul 2026
Apply before: 12 Aug 2026
Experience level: Senior
Views / Applies: 24 / 1

About company

About Truelogic

Accelerate Your Digital Transformation

Computer Software
2011

Actively Hiring

AI Summary

This Senior Data & Platform Engineer role at a veterinary technology company involves end-to-end ownership of a rapidly scaling data platform. You will design and maintain pipelines using Apache Airflow, manage Snowflake warehouses, and build dbt transformations. The position requires deep expertise in Kubernetes (EKS), Terraform, and CI/CD, along with strong SQL and Python skills. You'll collaborate cross-functionally to deliver clean data products and drive adoption of change data capture (CDC). This is a fully remote, high-ownership position within a nearshore staff augmentation firm.

Role DNA

Job Complexity

Easy Hard

Pace & Pressure

Relaxed Fast-paced

Autonomy Level

Guided Full Ownership

Communication Load

Independent Highly Collaborative

AI Insight Requires 5+ years of experience, deep expertise in multiple complex tools (Snowflake, dbt, Airflow, Kubernetes, Terraform), and end-to-end ownership of a data platform, making it highly challenging.

Salary Analysis

AI Insight The salary is not specified in the listing. Based on market data for a Senior Data Engineer with expertise in Snowflake, dbt, Airflow, and cloud infrastructure, the estimated median salary is $150,000, which is competitive for the US market.

Core Skills Required

Data Engineering Snowflake dbt Apache Airflow Kubernetes Terraform Python SQL AWS CI/CD

Cover Letter Sample

I am writing to express my strong interest in the Senior Data & Platform Engineer position at your veterinary technology company. With over 5 years of experience building production-grade data platforms from the ground up, I have deep expertise in Snowflake, dbt, and Airflow, as well as hands-on experience with Kubernetes and Terraform.

I have a proven track record of designing robust pipelines, optimizing data warehouses for cost and performance, and implementing comprehensive observability. I am particularly drawn to the opportunity to take end-to-end ownership of a rapidly scaling data platform that powers analytics for veterinary clinics worldwide.

I thrive in high-autonomy, fully remote environments and enjoy collaborating with cross-functional teams to deliver clean, well-documented data products. I am excited about leading the transition to change data capture and ensuring the reliability of complex data workflows.

I would welcome the chance to discuss how my skills can contribute to the continued success of your data platform. Thank you for your consideration.

Sample Interview Questions

Can you describe how you would optimize a Snowflake warehouse for both cost and performance in a high-volume data environment?

I would start by setting up appropriate warehouse sizes and auto-scaling, using multi-cluster warehouses for concurrency. I'd implement clustering keys on large tables, use materialized views for aggregations, and leverage time travel and zero-copy cloning carefully. For cost, I'd set resource monitors, use compression, and partition large tables with pruning. Also, I'd review query profiles to identify inefficiencies.

How would you design a dbt model for incremental loading of large fact tables?

I would define the model with an incremental materialization, using a unique key and a filtering condition like a timestamp column. I'd use the 'incremental_strategy' of 'merge' to avoid duplicates. For large tables, I'd consider using partitions in the source and adding a 'where' clause to only process new data. I'd also implement tests for freshness and uniqueness.

Describe your approach to debugging a stalled Airflow DAG. What steps would you take?

First, I'd check the task logs for any errors. Then I'd review the DAG's task dependencies and retries. If it's stuck, I'd check the scheduler and worker health, resource usage, and any deadlocks in the database. I would also look at the DAG's schedule interval and ensure the previous runs completed. If needed, I'd manually clear failed tasks or rerun from a specific point.

How would you troubleshoot a pod crash in a Kubernetes cluster running a data pipeline?

I'd start by describing the pod with 'kubectl describe pod' to see the last state and events. Then check logs with 'kubectl logs'. If resources are insufficient, I'd adjust resource requests/limits. I'd also check the node health and any network issues. For recurring crashes, I'd review the probe configuration (liveness/readiness) and consider if the application has a memory leak.

Can you explain your experience with change data capture (CDC) and how you would migrate from batch to CDC for data ingestion?

I have used Debezium with Kafka to capture changes from databases. For migration, I would first set up the CDC pipeline as a parallel stream while keeping the batch pipeline for historical loads. I'd monitor both for consistency. Once CDC is reliable, I'd backfill any missing data and gradually switch downstream consumers. Key considerations are schema evolution, handling large initial snapshots, and managing offsets.

About Truelogic

At Truelogic we are a leading provider of nearshore staff augmentation services headquartered in New York. For over two decades, we’ve been delivering top-tier technology solutions to companies of all sizes, from innovative startups to industry leaders, helping them achieve their digital transformation goals.

Our team of 600+ highly skilled tech professionals, based in Latin America, drives digital disruption by partnering with U.S. companies on their most impactful projects. Whether collaborating with Fortune 500 giants or scaling startups, we deliver results that make a difference.

By applying for this position, you’re taking the first step in joining a dynamic team that values your expertise and aspirations. We aim to align your skills with opportunities that foster exceptional career growth and success while contributing to transformative projects that shape the future.

Our Client

A leading innovator in the veterinary technology space, providing a comprehensive ecosystem of practice management software, AI tools, and clinical decision support used by professionals worldwide.

Job Summary

As a Senior Data Engineer, you will take end-to-end ownership of a rapidly scaling data platform, driving the ingestion, transformation, storage, and delivery of critical analytics for veterinary clinics. Working within a high-ownership, fully remote engineering team, you will ensure the health and reliability of complex pipelines, leverage modern infrastructure-as-code practices, and establish robust data quality standards. The ideal candidate is a battle-tested builder who has previously constructed data platforms from the ground up, seamlessly combining deep data warehousing expertise with hands-on infrastructure management.

Responsibilities

Take end-to-end ownership of the data platform’s reliability, encompassing pipelines, warehousing, transformations, and observability.
Design, build, and maintain robust data pipelines using Apache Airflow, orchestrating complex workflows across batch and near-real-time workloads.
Manage and optimize the primary Snowflake data warehouse, handling schema design, clustering keys, materialized views, access controls, and strict cost governance.
Build and maintain the dbt transformation layer, including model design, incremental strategies, dependency management, and documentation.
Deploy and operate data platform services on Kubernetes (EKS), independently managing workloads, debugging pod issues, and tuning resource requests.
Provision and manage data platform infrastructure, including Snowflake resources and Airflow, utilizing Terraform.
Write high-quality SQL and Python for ETL tooling, pipeline logic, and data product delivery.
Manage PostgreSQL as a source-of-truth operational database, focusing on query optimization, indexing, replication, and migrations.
Lead the transition toward change data capture (CDC) for data ingestion, utilizing tools like Debezium to stream database changes.
Implement comprehensive observability, SLA tracking, and alerting across the data platform using Datadog.
Maintain CI/CD pipelines for DAG deployments, dbt runs, schema migrations, and container image builds.
Collaborate with analytics, product, and full-stack development teams to model clean, well-documented data products.
Navigate AWS environments (including RDS and S3) to manage data-adjacent services, read logs, and adjust scaling parameters.

Qualifications and Job Requirements

5+ years of experience in data engineering or a combined data and infrastructure role, with a proven track record of building production-grade data platforms from the ground up.
Deep expertise in Snowflake, including schema design, performance tuning, access control, and advanced cost optimization.
Strong, hands-on experience with dbt (modeling patterns, incremental strategies, testing) and Airflow (writing DAGs, managing dependencies, debugging).
Advanced proficiency in SQL and solid programming skills in Python for ETL and automation.
Working knowledge of Kubernetes, with the ability to deploy workloads, read pod logs, and manage Helm charts independently.
Extensive experience using Terraform for infrastructure provisioning, specifically for managing data platform components.
Hands-on experience with Datadog for observability, monitoring, and SLA visibility.
Familiarity with AWS data-adjacent services like RDS and S3.
Fluency in utilizing AI-based development assistants to write, review, and debug code efficiently.
Prior experience with Amazon Redshift is a plus.
Familiarity with streaming infrastructure (Kafka, Flink) or open table formats (Iceberg, Delta Lake) is highly preferred.
Experience with Elixir or Erlang is advantageous.
Knowledge of data quality, cataloging, or lineage tooling (e.g., Great Expectations, Soda, Monte Carlo, DataHub, OpenMetadata) is a strong plus.
Hands-on experience with Debezium or comparable CDC tooling, particularly in production deployments, is a strong bonus.
Experience managing EKS Spot instances at scale is a plus.
Background in healthcare, veterinary medicine, or similarly regulated domains requiring strict data accuracy and auditability is preferred.

What We Offer

100% Remote Work: Enjoy the freedom to work from the location that helps you thrive. All it takes is a laptop and a reliable internet connection.
Highly Competitive USD Pay: Earn an excellent, market-leading compensation in USD, that goes beyond typical market offerings.
Paid Time Off: We value your well-being. Our paid time off policies ensure you have the chance to unwind and recharge when needed.
Work with Autonomy: Enjoy the freedom to manage your time as long as the work gets done. Focus on results, not the clock.
Work with Top American Companies: Grow your expertise working on innovative, high-impact projects with Industry-Leading U.S. Companies.

Why You’ll Like Working Here

A Culture That Values You: We prioritize well-being and work-life balance, offering engagement activities and fostering dynamic teams to ensure you thrive both personally and professionally.
Diverse, Global Network: Connect with over 600 professionals in 25+ countries, expand your network, and collaborate with a multicultural team from Latin America.
Team Up with Skilled Professionals: Join forces with senior talent. All of our team members are seasoned experts, ensuring you’re working with the best in your field.

Apply now!

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