Data Engineer

Kairos; Irving (TX); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Kairos is seeking a hands-on Data Engineer in Irving, TX to own well‑defined data components from development through production, collaborating with senior engineers and architects on design decisions.

You will build scalable data pipelines using Python, SQL, Spark/PySpark, and integrate enterprise data with AI‑generated insights for decision‑making, while ensuring data quality and security practices across environments. Hybrid schedule in Irving enables close collaboration with product partners.

Qualifications

• Bachelor’s degree or equivalent practical experience in a quantitative field.
• 10 years of relevant data engineering or software engineering experience.
• Strong Python and SQL proficiency with complex queries and troubleshooting.
• Experience with Spark/PySpark, cloud ETL/ELT on Azure or AWS.

Responsibilities

• Build, maintain, and enhance scalable data pipelines using Python, SQL, Spark/PySpark.
• Integrate enterprise data from applications, APIs, event streams and AI outputs.
• Develop transformations across structured and semi-structured data with time zones.
• Create data models and curated datasets for analytics and AI model development.
• Collaborate with AI/ML engineers to deliver data interfaces for inference workflows.
• Implement data quality checks, monitoring, and alerting for data freshness and traceability.
• Troubleshoot pipeline failures; implement retries, backfills, and cost-aware processing.
• Work with product managers and engineers to translate requirements into data solutions.
• Follow security, privacy, and retention requirements for enterprise data.

Skills

Python

SQL

Spark/PySpark

ETL/ELT

Airflow

Databricks

CI/CD

Data modeling

Cloud platforms

Data governance

Education

Bachelor’s degree in Computer Science or related field

Tools

Databricks

Delta Lake

Kafka

Git

CI/CD tools

Job description

Locals Only Data engineers, AI/ML engineers, data scientists, and product partners building enterprise data and AI solutions. In this role, you’ll build reliable data foundations that power AI-enabled products, operational intelligence, and business decision‑making. You’ll integrate enterprise data with AI‑generated insights and contribute reusable data capabilities across Enterprise AI initiatives. This is a hands‑on individual contributor role. You’ll own well‑defined data engineering components from development through production, working with senior engineers and architects on solution design and technical direction.

Location: Irving , TX ----- Hybrid

Duration: 12+ Months

Locals Only Data engineers, AI/ML engineers, data scientists, and product partners building enterprise data and AI solutions. In this role, you’ll build reliable data foundations that power AI-enabled products, operational intelligence, and business decision‑making. You’ll integrate enterprise data with AI‑generated insights and contribute reusable data capabilities across Enterprise AI initiatives. This is a hands‑on individual contributor role. You’ll own well‑defined data engineering components from development through production, working with senior engineers and architects on solution design and technical direction.

Key Responsibilities

• Build, maintain, and enhance scalable batch and near‑real‑time data pipelines using Python, SQL, and Spark/PySpark.
• Integrate data from enterprise applications, operational systems, APIs, event streams, and AI inference outputs into reliable, analysis‑ready datasets.
• Develop transformations that combine structured and semi‑structured data across sources using consistent identifiers and timestamps; handle time zones, duplicate records, and late‑arriving events.
• Create and maintain data models and curated datasets for business analytics, AI model development and evaluation, and AI‑enabled application workflows.
• Partner with AI/ML engineers to deliver data interfaces for inference workflows and capture model outputs, confidence scores, and user feedback for analysis and continuous improvement.
• Implement data quality checks, reconciliation, pipeline monitoring, and alerting to ensure completeness, accuracy, freshness, and traceability.
• Troubleshoot pipeline failures and performance issues; implement reliable retries, backfills, and incremental processing while optimizing compute and storage costs.
• Collaborate with product managers, data scientists, analysts, and engineering partners to translate business requirements into practical data solutions.
• Follow data security, privacy, access‑control, and retention requirements when handling enterprise data and AI-generated outputs.
• Write maintainable, tested code; participate in code reviews and CI/CD practices; document schemas, data lineage, dependencies, and operational runbooks.

Basic Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Engineering, Mathematics, Statistics, or a related quantitative discipline, or equivalent practical experience.
• 10 years of relevant experience in data engineering or a closely related software engineering role.
• Strong hands‑on proficiency in Python and SQL, including complex queries, transformations, and troubleshooting.
• Experience with Spark/PySpark and distributed data processing.
• Experience building and supporting ETL/ELT pipelines on a cloud platform such as Azure or AWS.
• Understanding of data modeling, relational databases, data lakes, data warehouses, and incremental processing patterns.
• Experience with workflow orchestration tools such as Apache Airflow or Databricks Workflows.
• Familiarity with event‑driven or streaming data processing concepts, including event time, deduplication, and reliable delivery.
• Experience with Git, automated testing, and production monitoring or troubleshooting.
• Ability to independently deliver scoped work, communicate technical concepts clearly, and collaborate with technical and business stakeholders.
• Hands‑on experience with Databricks, Delta Lake, or lakehouse architectures.
• Experience with Databricks Asset Bundles / Declarative Automation Bundles to define, version, and deploy Databricks jobs and pipelines across environments through CI/CD.
• Experience with streaming technologies such as Kafka, Azure Event Hubs, or Spark Structured Streaming.
• Experience with retail, customer, inventory, or operational datasets.
• Experience preparing datasets for machine learning, anomaly detection, or AI model evaluation.
• Familiarity with multimodal data, unstructured data processing, or time‑based event correlation.
• Exposure to edge‑to‑cloud data integration, containerized workloads, or infrastructure as code.