Data Engineer (Temporal & Apache Kafka required)

About Infinitive
Infinitive is a data and AI consultancy that helps clients modernize monetize and operationalize their data to generate lasting value. They pride themselves on their deep industry and technology expertise ensuring that they drive and sustain the adoption of new capabilities. Infinitive is committed to aligning their team with their clients culture ensuring a successful partnership by bringing the right mix of talent and skills for high return on investment.

Infinitive has earned recognition as one of the Best Small Firms to Work For by Consulting Magazine receiving this accolade nine times most recently in 2026. They have also been honored as a Top Workplace by the Washington Post Best Places to Work by the Washington Business Journal and Best Places to Work by Virginia Business.

About the Role

We are seeking an experienced Data Engineer to help design build and scale our next-generation event-driven data 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.

• Stream Processing & Messaging: Architect deploy and maintain high-volume distributed data streams using Apache Kafka (producers consumers Kafka Connect Schema Registry).

• Data Schema Design & Validation: Establish and enforce schema design standards versioning strategies and automated schema validation (e.g. Avro Protobuf JSON Schema) to maintain strict data contracts across microservices streaming consumers and lakehouse storage.

• Resilient Workflow Orchestration: Design and implement durable execution workflows using Temporal to coordinate long-running distributed pipelines compensate transactions (Saga pattern) and manage cross-system ETL tasks.

• Pipeline Development: Build end-to-end batch and near-real-time pipelines using Python SQL and Apache Spark / PySpark.

• Data Modeling & Warehousing: Design and optimize analytical data models (dimensional/star schema) in modern cloud data warehouses/lakehouses (e.g. Snowflake BigQuery Databricks Redshift).

• Reliability & Data Quality: Implement automated testing continuous schema validation data drift detection and observability across streaming and batch workflows.

• Cross-Functional Collaboration: Partner with software engineers machine learning engineers and analysts to define standard schema definitions data contracts and production-grade CI/CD release patterns.

Required Experience

• 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.