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Data Infrastructure Engineer

Posted 1 day agoViewed

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💎 Seniority level: Senior, 5+ years

📍 Location: United States

🔍 Industry: Software Development

🏢 Company: Worth AI👥 11-50💰 $12,000,000 Seed over 1 year agoArtificial Intelligence (AI)Business IntelligenceRisk ManagementFinTech

⏳ Experience: 5+ years

Requirements:
  • Strong programming skills in Python, Node.js.
  • Proficient in SQL and experience with distributed query engines (e.g., Trino, Presto).
  • Experience with cloud-native data platforms such as AWS Glue
  • Hands-on experience with infrastructure-as-code tools (Terraform, Pulumi, CloudFormation).
  • Familiarity with containerization and orchestration tools such as Kafka and Kubernetes.
  • Solid understanding of data governance, quality frameworks, and data lifecycle management.
  • Experience in streaming data architecture and tools like Apache Kafka, Kinesis, or Pub/Sub.
  • Background in supporting machine learning or analytics platforms.
  • Exposure to data mesh, data contracts, or modern data stack concepts.
  • Knowledge of DevOps principles applied to data systems.
Responsibilities:
  • Design, build, and maintain scalable and resilient data infrastructure in a cloud environment (AWS, Azure, or GCP).
  • Develop and maintain ETL/ELT pipelines using orchestration tools such as Airflow, Dagster, or dbt.
  • Optimize data workflows for reliability, performance, and cost efficiency across structured and unstructured datasets.
  • Manage data lake and data warehouse environments (e.g., Snowflake, BigQuery, Redshift, Delta Lake).
  • Ensure data security, privacy, and compliance, including role-based access control, data encryption, and audit logging.
  • Collaborate with data scientists, analysts, and product teams to ensure data accessibility, accuracy, and availability.
  • Support real-time and batch data processing frameworks, including Kafka, Spark, Flink, or similar tools.
  • Monitor, troubleshoot, and improve the observability and performance of data systems using tools like Prometheus, Grafana, or Datadog.
  • Maintain CI/CD pipelines for data infrastructure using Terraform, GitHub Actions, or similar tools.
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🔥 Data Infrastructure Engineer
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🏢 Company: Intellectsoft👥 251-500Augmented RealityArtificial Intelligence (AI)DevOpsBlockchainInternet of ThingsUX DesignWeb DevelopmentMobile AppsQuality AssuranceSoftware

  • 5+ years of professional engineering and data analytics background, preferably with some time spent in a startup environment.
  • Advanced SQL and Python abilities for managing complex data analysis.
  • Demonstrated aptitude to create automation tools and pipelines utilizing Python, Golang, and/or Typescript.
  • Practical experience with data pipeline and warehouse technologies such as Snowflake, Databricks, Apache Spark, and AWS Glue.
  • Skill in formulating declarative data models and transformations utilizing modern tools such as dbt.
  • Experience in building and overseeing cloud-based data lakes.
  • Previous experience incorporating real-time data streaming technologies such as Kafka and Spark.
  • Prior experience setting up and upholding modern data orchestration platforms such as Airflow.
  • Familiarity with infrastructure-as-code tools like Terraform and container orchestration platforms like Kubernetes.
  • Design, construct, and uphold contemporary and resilient real-time and batch data analytics pipelines.
  • Formulate and sustain declarative data models and transformations.
  • Put into effect data ingestion integrations for streaming and conventional sources such as Postgres, Kafka, and DynamoDB.
  • Set up and arrange BI tooling for data analysis.
  • Collaborate closely with product, finance, legal, and compliance teams to construct dashboards and reports to support business operations, regulatory obligations, and customer needs.
  • Institute, convey, and impose data governance policies.
  • Record and disseminate optimal procedures concerning schema management, data integrity, availability, and security.
  • Safeguard and restrict access to sensitive data by implementing a secure permissioning model and establishing data masking and tokenization processes.
  • Recognize and convey data platform needs, including supplementary tooling and staffing.
  • Cooperate with cross-functional teams to delineate requirements, strategize projects, and execute the strategy.
Posted 30 days ago
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