Data Engineer (Only W2)

About the Job

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.

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.

· 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.