Senior Data Engineer with AI

We’re hiring an exceptional Senior Data Engineer with deep data engineering expertise and strong, hands-on AI skills. You’ll build scalable data infrastructure across ingestion, transformation, orchestration, and delivery - collaborating with backend, BI, and ML teams - while actively using AI tooling (Claude, Cursor, and beyond) as a core part of how you engineer and ship. Strong ownership mindset, data quality focus, on-call reliability, and a genuine drive to adopt modern AI tooling are a must. Responsibilities · Design, build, and maintain scalable data ingestion and processing pipelines using SQL , Spark, Hadoop, Airflow, SnapLogic, or similar tools · Optimize existing ETL/ELT workflows and resolve bottlenecks in production jobs · Integrate multiple structured and unstructured data sources from internal and external systems · Design and implement event-driven data pipelines using event bus architectures (Kafka, AWS EventBridge, SNS/SQS, or similar) · Build and maintain event producers, consumers, and stream processors for real-time and near-real-time data flows · Manage event schema design, schema registry, and versioning to ensure backward compatibility across consumers · Monitor event bus health, lag, throughput, and reliability; respond to failures before SLA breach · Implement and manage CI/CD pipelines for data workflows using Git, Jenkins, or GitHub Actions · Ensure robust job scheduling, monitoring, logging, and end-to-end testing of data pipelines · Debug and resolve production issues before SLA breach; participate in on-call rotation · Collaborate with backend and BI teams for API and data integration · Design and develop schema definitions; support data warehouse/mart to integrate disparate data sources and make them available for analysis · Support large data volumes and flexible provisioning of new data sources · Contribute to data architecture design across the data landscape · Write clean, reusable, well-tested Python and Shell code across the full software development lifecycle · Gather functional requirements, develop technical specifications, and own project and test planning · Work in Agile/Scrum environments; help align team priorities across the organization · Leverage AI-assisted dev tools (Claude, Cursor .. etc) to accelerate delivery and code quality

Artificial Intelligence (AI), Artificial Intelligence (AI), Scalable System Development, Business Intelligence, Machine Tool, On Call, Data Quality, Apache Hadoop, Apache Spark, SQL (Structured Query Language), · Strong Python, Advanced SQL, PySpark or Scala, Unix scripting · Data orchestration: Airflow, SnapLogic, DBT, or equivalent · Event bus & streaming: Kafka, AWS EventBridge, SNS/SQS, Kinesis, or similar · Event-driven architecture: producers, consumers, schema registry, dead-letter queues · AWS cloud: EC2, EMR, S3, Athena, Glue; Databricks · Data lake & warehouse: Snowflake, Delta Lake, Redshift, BigQuery · CI/CD tooling: Git, Jenkins / GitHub Actions, Docker · Experience with APIs for data exchange and integration · Data quality mindset, ownership, and SLA awareness · NoSQL, Neo4j, GraphQL is plus · Agile/Scrum development experience Preferred / Nice to Have · Experience with AI/ML models and feature pipelines · Exposure to BI platforms (Tableau, Power BI) or data serving for dashboards · Spark Streaming or Flink for stateful stream processing Skills Data Engineer, Spark, AWS, Airflow, ETL, Amazon Web Services Datalake, Python PySpark, SNS, SQS, Confluence, Jira