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Senior Data Engineer, Data Platform

Tronic is at the forefront of transforming how enterprises connect with their customers. We specialize in advanced brand loyalty and customer engagement solutions, leveraging data-driven insights to build meaningful, lasting customer relationships. Our platform is designed to handle large-scale data across multiple touchpoints, providing businesses with deep analytics and actionable insights.

We’re looking for a Senior Data Engineer who will play a pivotal role in managing our data platform and analytics infrastructure. This role is critical in ensuring the reliability, performance, and scalability of our data ecosystem, which powers our loyalty and customer engagement solutions for enterprises across industries like retail, hospitality, and consumer goods.

What You’ll Do:

As a Senior Data Engineer, you will oversee the data architecture, data pipeline performance, and scalability of our customer data platform. You will work closely with the engineering, analytics, and product teams to design and maintain the infrastructure for data pipelines, data lakes, and large-scale OLAP databases.

  • Design, deploy, and manage OLAP databases at scale (e.g., ClickHouse, Amazon Redshift, Snowflake, Postgres) for high-performance analytics across large data volumes.

  • Implement and optimize event streaming pipelines using Kafka to process and manage real-time data streams.

  • Manage and scale Elasticsearch clusters for efficient indexing and retrieval of large datasets.

  • Ensure data governance and schema management practices are followed to maintain data quality, security, and compliance.

  • Develop and maintain data lake architectures using AWS technologies like Amazon S3 and AWS Glue, ensuring efficient ingestion, storage, and retrieval of customer data from multiple sources.

  • Collaborate with product teams to define data schema and manage schema evolution to support new features and analytics.

  • Ensure the availability, security, and performance of the data platform, monitoring key performance metrics, and proactively addressing any issues.

  • Automate infrastructure and data pipeline deployments using Terraform, AWS CloudFormation, or Ansible.

  • Implement backup, disaster recovery, and failover procedures for mission-critical data systems using AWS services like Amazon RDS, AWS Backup, and AWS Lambda.

  • Assist in building data governance frameworks, ensuring compliance with privacy regulations (e.g., GDPR, CCPA) and industry standards.

  • Support and optimize SQL and NoSQL databases to meet high-volume, high-velocity data processing needs.

  • Collaborate with engineering and data science teams to ensure efficient data flow and integration across systems.

Our Tech Stack:

  • OLAP Databases: ClickHouse, Amazon Redshift, Snowflake, Postgres for high-performance analytics.

  • Event Streaming: Kafka for real-time data ingestion and processing.

  • Search and Indexing: Elasticsearch for scalable search and analytics on large datasets.

  • Data Pipelines: Apache Spark, AWS Glue, and Airflow for data orchestration and transformation.

  • Cloud Infrastructure: AWS with core services including Amazon S3, EC2, EKS (Kubernetes), AWS Lambda, RDS, Amazon Redshift, DynamoDB, and CloudFormation for cloud orchestration.

  • Automation Tools: Terraform, Ansible, AWS CloudFormation, and Docker for automating infrastructure provisioning and deployments.

  • CI/CD Pipelines: Managed using GitHub Actions, Jenkins, and CircleCI.

  • Data Governance & Compliance: Implementing industry best practices for data security, privacy, and schema evolution.

What We’re Looking For:

We’re seeking an experienced Data Engineer who is passionate about data architecture and scalability and who has extensive experience managing data platforms for customer analytics.

  • Proven experience designing and managing OLAP databases at scale, with a deep understanding of their performance and query optimization.

  • Expertise in managing and scaling event streaming pipelines with Kafka or similar tools.

  • Experience deploying and optimizing Elasticsearch for search and analytics across large data volumes.

  • Solid understanding of data governance and schema management, with experience implementing data security, privacy, and compliance frameworks (e.g., GDPR, CCPA).

  • Hands-on experience managing data lake architectures using AWS technologies like Amazon S3, AWS Glue, and Lambda, and implementing scalable, performant ETL pipelines.

  • Strong knowledge of SQL and NoSQL databases and how to optimize them for high-throughput and low-latency operations.

  • Familiarity with Terraform, Ansible, Docker, and Kubernetes for managing and automating cloud infrastructure.

  • Experience with cloud platforms (AWS preferred) and managing infrastructure as code.

  • Ability to collaborate with cross-functional teams, including engineering, product, and data science teams, to ensure the smooth operation of the data platform.

  • Strong problem-solving skills and the ability to think strategically about long-term data architecture and governance.

  • Passion for data analytics, with an understanding of how to build scalable systems that support high-volume customer analytics and insights.

What We Offer:

At Tronic, we believe in fostering a culture of innovation and collaboration. We provide a dynamic environment where you can grow your career and work on cutting-edge technologies that impact millions of users.

  • The opportunity to work on large-scale, complex data platforms that drive business insights for global brands.

  • Competitive salary and equity options, ensuring you're rewarded for your contributions to our success.

  • Remote work opportunities with flexible hours to maintain a healthy work-life balance.

  • Comprehensive health, dental, and vision insurance.

  • Generous and flexible time off policies.

  • A team-first culture where your ideas and expertise are valued, and you can make a direct impact on the future of our platform.

If you’re passionate about building and scaling high-performance data platforms and want to work with some of the best minds in the industry, join Tronic and help us shape the future of customer data and analytics.

Average salary estimate

$125000 / YEARLY (est.)
min
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$100000K
$150000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Senior Data Engineer, Data Platform, Tronic

At Tronic, we’re on a mission to revolutionize how enterprises connect with their customers, and a key player in this journey is our Senior Data Engineer for the Data Platform team in Dallas. In this pivotal role, you will be charged with managing our sophisticated data architecture and analytics infrastructure that drives our innovative brand loyalty solutions. You will collaborate closely with engineering, analytics, and product teams to design high-performance data pipelines, data lakes, and OLAP databases that support real-time analytics. If you have a passion for data architecture, a knack for problem-solving, and a desire to work with cutting-edge technologies, you’ll feel right at home with us. Your day-to-day will involve deploying OLAP databases, optimizing event streaming pipelines with Kafka, and managing large datasets with Elasticsearch. You will also ensure data governance, security, and compliance by building frameworks that align with industry standards like GDPR and CCPA. A top priority will be to develop a robust and scalable infrastructure utilizing AWS services for high data availability and efficient ingestion processes. With a strong focus on collaboration and innovation, Tronic provides a nurturing environment where your expertise is valued, and your contributions can have a substantial impact. If you’re excited about shaping the future of data analytics, we would love to see you join our talented team as a Senior Data Engineer!

Frequently Asked Questions (FAQs) for Senior Data Engineer, Data Platform Role at Tronic
What are the primary responsibilities of a Senior Data Engineer at Tronic?

As a Senior Data Engineer at Tronic, your main responsibilities will include overseeing the data architecture and performance of our customer data platform, managing large-scale data pipelines, and collaborating with various teams to ensure seamless data flow across systems. You will focus on deploying and optimizing OLAP databases, implementing real-time event streaming with Kafka, and ensuring data governance and compliance with industry regulations. Additionally, you will be instrumental in developing and maintaining data lake architectures using AWS services.

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What qualifications are needed for a Senior Data Engineer role at Tronic?

To be considered for the Senior Data Engineer position at Tronic, you should possess extensive experience in designing and managing OLAP databases at scale, expertise in Kafka or similar event streaming technologies, and proficiency in data governance frameworks. Additionally, familiarity with AWS technologies, data lake architectures, SQL, NoSQL databases, and infrastructure automation with tools like Terraform and Ansible will make you an ideal candidate for this role.

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How does Tronic ensure data compliance for the Senior Data Engineer position?

At Tronic, ensuring data compliance is a critical responsibility for the Senior Data Engineer. You will be expected to implement frameworks that adhere to privacy regulations such as GDPR and CCPA. This involves maintaining data quality, enforcing security measures, and developing comprehensive backup and disaster recovery procedures to protect sensitive customer data. Additionally, collaborating with cross-functional teams will help align the data platform with organizational compliance standards.

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What technology stack does the Senior Data Engineer at Tronic work with?

As a Senior Data Engineer at Tronic, you will work with a rich technology stack that includes OLAP databases such as ClickHouse, Amazon Redshift, and Snowflake. You will also utilize Apache Kafka for event streaming, Elasticsearch for indexing and search capabilities, and various AWS services like Amazon S3, AWS Glue, and AWS Lambda for data storage and processing. This tech stack will allow you to build scalable data solutions that drive analytics insights.

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What career opportunities can a Senior Data Engineer expect at Tronic?

A Senior Data Engineer at Tronic will find ample opportunities for career advancement. Working with cutting-edge technologies and on large-scale data platforms offers a path to grow within the organization, whether it's taking on leadership roles, specializing in data architecture, or transitioning into related fields such as data science or product management. Tronic embraces a culture of continuous learning and professional development, making it an excellent environment for ambitious individuals.

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Common Interview Questions for Senior Data Engineer, Data Platform
Can you describe your experience with OLAP databases?

In answering this question, emphasize your hands-on experience with OLAP databases, specifically detailing the types you've worked with, such as Amazon Redshift or Snowflake. Discuss specific projects where you designed or managed these databases, any challenges you encountered, and the optimizations you implemented to improve query performance.

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How do you manage and scale event streaming pipelines?

Begin your response by outlining your experience with technologies like Kafka for event streaming. Discuss specific strategies you’ve used to manage and scale pipelines, such as monitoring system performance metrics, optimizing configurations for scalability, and implementing best practices for fault tolerance and data consistency.

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Explain your approach to ensuring data quality and governance.

To effectively answer this question, share examples of frameworks or processes you've implemented to uphold data quality and governance. Discuss how you ensure data accuracy, privacy compliance, and adherence to data management standards, possibly referencing GDPR or CCPA to showcase your compliance familiarity.

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What role does AWS play in your data engineering projects?

Your answer should reflect your expertise with AWS and its services. Detail specific projects where you utilized AWS tools such as S3, Glue, or Lambda for data processing. Highlight any architectural designs you’ve implemented and how using AWS has improved scalability, availability, and performance in your projects.

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Can you share your experience with data lake architectures?

When responding, describe any data lake architectures you’ve worked with, including the tools and frameworks you've used for data ingestion and storage. Discuss the benefits of using a data lake for analytics, mentioning the challenges you faced and how you overcame them to enhance performance and accessibility of data.

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Describe how you automate data pipeline deployments.

Highlight the tools you've used for automating deployments, such as Terraform, Ansible, or AWS CloudFormation. Discuss a specific instance where you automated a data pipeline deployment, the reasoning behind your approach, and how it improved your team's productivity and deployment reliability.

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What methods do you use to monitor the performance of data pipelines?

Explain the various performance monitoring techniques you leverage, such as utilizing logging tools, performance metrics dashboards, or alerting systems. Share a situation where monitoring helped you identify issues proactively, detailing the outcome of your actions on pipeline performance.

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How do you handle schema evolution in your data services?

Discuss your strategies for managing schema evolution, such as version control practices, automated migration scripts, and backward compatibility techniques. Provide an example of a schema change you managed and how it affected system performance and data integrity.

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How do you handle collaboration with cross-functional teams?

In your answer, share experiences where you worked closely with engineering, analytics, or product teams. Describe your approach to communication, shared goals, and ensuring that data solutions align with business objectives, reflecting on any successful outcomes from these collaborations.

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What do you enjoy most about working in data engineering?

Here, reflect your passion for the field. Discuss aspects like solving complex problems, working with data at scale, or your commitment to continual learning. Share examples of inspirational projects or experiences that fueled your enthusiasm for data engineering.

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MATCH
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EMPLOYMENT TYPE
Full-time, remote
DATE POSTED
March 26, 2025

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