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# Data Engineer

- **Company:** SIGNALFIRE IV GP, L.L.C.
- **Location:** United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Adaptable Database Systems, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Microsoft Azure, BigQuery, Cloud Computing, Computer Programming, Databases, Data Architecture, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Data Warehousing, Distributed Systems, Amazon DynamoDB, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, MySQL, Scala (Programming Language), Software Engineering, SQL Databases, Data Streaming, Workflow Management Systems, Google Cloud, Data Storage Technologies, Snowflake, Apache Spark, Data Lakes, Kubernetes, Apache Flink, Apache Kafka, Data Management, Terraform, Data Pipelines, Docker, Amazon Redshift, Databricks, Golang
- **Published:** August 8, 2026
- **Apply:** https://www.dice.com/job-detail/08668c9c-891c-43fb-9f86-4878905c858e

## About the Role

? Passionate about building reliable, scalable data systems and infrastructure
? Experienced in transforming complex datasets into trusted, accessible data products
? Excited to establish data foundations in fast-moving startup environments
? Comfortable partnering with engineering, product, analytics, and machine learning teams
? Interested in improving how data is collected, modeled, governed, and used across an organization, * 3+ years of experience in data engineering, software engineering, analytics engineering, or a related technical role

 * Strong programming skills in Python, Java, Scala, or a similar language
 * Advanced proficiency in SQL and experience designing scalable data models
 * Experience building and maintaining production ETL or ELT pipelines
 * Familiarity with cloud platforms such as AWS, Google Cloud Platform, or Azure
 * Experience with modern data warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, or Databricks
 * Knowledge of workflow orchestration, transformation, and data-quality tooling
 * Understanding of distributed systems, data storage formats, and batch or streaming architectures
 * Ability to collaborate with technical and non-technical stakeholders to translate business needs into data solutions
 * Strong judgment around reliability, scalability, governance, and infrastructure tradeoffs
 * Experience in venture-backed startups or rapidly scaling technology companies may be preferred

Technologies You Might Work With:

 * Languages: Python, SQL, Java, Scala, Go
 * Warehouses & Lakehouses: Snowflake, BigQuery, Redshift, Databricks, Delta Lake
 * Pipelines & Transformation: Airflow, Dagster, Prefect, dbt, Fivetran, Airbyte
 * Streaming & Processing: Kafka, Spark, Flink, Kinesis, Pub/Sub
 * Cloud & Infrastructure: AWS, Google Cloud Platform, Azure, Docker, Kubernetes, Terraform
 * Data Quality & Observability: Great Expectations, Monte Carlo, Soda, DataHub, OpenLineage
 * Databases & Storage: PostgreSQL, MySQL, DynamoDB, MongoDB, S3

## Description

* Design, build, and maintain scalable batch and real-time data pipelines
 * Develop reliable data models, transformation workflows, and shared datasets for analytics and operational use cases
 * Build and manage cloud-based data warehouses, lakehouses, and data platforms
 * Integrate data from product, customer, financial, and third-party systems
 * Establish standards for data quality, testing, lineage, observability, and documentation
 * Partner with analytics, product, engineering, and business teams to understand data requirements
 * Support machine learning and AI applications by developing dependable training, feature, and inference data pipelines
 * Improve the performance, scalability, and cost efficiency of data infrastructure
 * Build self-service tools and frameworks that make data easier to discover and use
 * Implement appropriate access controls, privacy safeguards, and data-governance practices
 * Troubleshoot pipeline failures, data-quality issues, and performance bottlenecks
 * Help define the company's broader data architecture and technical roadmap, 2. We review applications on an ongoing basis to identify strong candidates.
 3. If there's a match, a SignalFire talent partner or a leader from one of our startups may reach out directly.
 4. No match yet? We'll keep your profile on file for future Data Engineering roles across our portfolio.


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