Data Engineer (Temporal & Apache Kafka required)

Infinitive Inc; McLean (VA); On-site; USD 104,000 - 140,000; Full time

Job summary

Infinitive Inc in the United States is seeking an experienced Data Engineer to design, build, and scale event-driven data platforms. You will bridge real-time streaming with durable workflows using Kafka and Temporal, focusing on strict data contracts and robust schema design.

You will develop batch and streaming ETL/ELT pipelines with Python, Spark, and cloud data warehouses, ensure data quality, and collaborate across teams to deliver reliable, high-throughput data infrastructure.

Qualifications

• 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: proficiency with schema definition frameworks (Avro, Protobuf/gRPC, or JSON Schema).
• 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 and testing standards.
• Distributed computing experience: hands-on development with Apache Spark (PySpark/Spark SQL).
• Advanced SQL & Data Modeling: Relational databases, dimensional modeling, performance tuning.

Responsibilities

• 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 standards, versioning, and automated validation across microservices, streaming consumers, and lakehouse storage.
• Resilient Workflow Orchestration: Design durable execution workflows using Temporal to coordinate long-running distributed pipelines and 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 in cloud data warehouses/lakehouses (Snowflake, BigQuery, Databricks, Redshift).
• Reliability & Data Quality: Implement automated testing, continuous schema validation, data drift detection, and observability across workflows.
• Cross-Functional Collaboration: Partner with software engineers, ML engineers, and analysts to define data contracts and CI/CD release patterns.

Skills

Temporal

Apache Kafka

Python

SQL & Data Modeling

Spark / PySpark

Data quality & validation

Tools

Confluent Schema Registry

dbt

Great Expectations

Pydantic

Job description

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 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.

Key Responsibilities

• 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.

Compensation and Equal Opportunity

Infinitive is required by law in some jurisdictions to include a reasonable estimate of the compensation range for this role. The determination of this range includes various factors not limited to skill set, level, experience, relevant training, and licensure and certifications. Compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range for this role in the U.S. is $90,000 - $154,00.00.

Infinitive is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.