Data Engineer (Temporal & Apache Kafka required)

We are seeking an experienced Data Engineer to help design, build, and scale our next-generation event-driven data platforms. In this role, you will be instrumental in bridging high-throughput distributed streaming with complex, fault-tolerant workflow orchestration and strict data governance.

You will work extensively with Apache Kafka for real-time event streaming and Temporal (the open-source, durable execution engine originating from Uber/Cadence) to build resilient, distributed stateful workflows and data pipelines. A core focus of this position is establishing robust data schema design and automated validation to ensure strong data contracts across distributed systems. Alongside these technologies, you will design robust batch and streaming ETL/ELT pipelines leveraging Python, Apache Spark, and modern cloud data warehouses/lakehouses.

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• 4+ years of professional experience in data engineering, backend distributed systems, or software engineering.

• Hands-on experience with Temporal (or Cadence): Proven understanding of durable workflows, activities, retries, signals, queries, and long-running distributed task orchestration.

• Deep expertise with Apache Kafka: Practical experience with message partitioning, consumer groups, offset management, and topic design.

• Strong background in Data Schema Design & Validation:

  • Demonstrated proficiency with schema definition frameworks (Apache Avro, Protocol Buffers/gRPC, or JSON Schema).

  • Practical experience managing schema evolution, compatibility modes (backward/forward/full), and schema registries (e.g., Confluent Schema Registry, AWS Glue Schema Registry).

  • Experience enforcing data validation rules, contract testing, and data quality checks (e.g., Great Expectations, Pandera, Pydantic, dbt tests).

• Strong programming proficiency in Python (Go or Java is a plus) with clean code, design patterns, and unit/integration testing standards.

• Distributed computing experience: Hands-on development with Apache Spark (PySpark/Spark SQL) processing large-scale datasets.

• Advanced SQL & Data Modeling: Strong experience with relational databases, dimensional data modeling, and query performance tuning.