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# Data Engineer (Only W2)

- **Company:** ResolveTech Solutions Inc.
- **Location:** Irving, TX, United States
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Code Review, Information Systems, Continuous Integration, Data Deduplication, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Security, Data Systems, Data Warehousing, Relational Databases, Database Queries, Distributed Computing Environment, Python (Programming Language), Machine Learning, AI-enabled Products, Software Engineering, SQL Databases, Data Streaming, Unstructured Data, Enterprise Data Management, Data Server Interface, Enterprise Software Applications, Cloud Platform System, Apache Spark, Git, Containerization, Data Lakes, Pyspark, Semi-structured Data, Information Technology, Data Lineage, Apache Kafka, Operational Systems, Spark Streaming, Data Management, Video Streaming, Model Inference, Data Pipelines, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.dice.com/job-detail/8eab815a-a38c-40ed-8560-9142391b3201

## About the Role

· Bachelor's degree in Computer Science, Software Engineering, Information Systems, Engineering, Mathematics, Statistics, or a related quantitative discipline, or equivalent practical experience.

· 3-5 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.

Extra Credit

· 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.

## Description

You'll join a multidisciplinary team of 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., · 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.