Tekaccel, Inc

Tekaccel, Inc

Data Engineer - Google Cloud Platform || Phoenix, AZ

Phoenix, Arizona

Sponsorship not specifiedDetected 7 days ago
PythonGitSQLBigQueryGCPCloud PlatformsCI/CDSparkAirflowData EngineeringCybersecurityAgile

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odds of building a lasting career here

40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

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Employer immigration record

from this employer's Department of Labor filings

Third-party placement is common here

34% of this employer's visa filings place the worker at a client company rather than at the employer itself, across 34 clients. Your visa, salary band and layoff exposure would follow the employer of record, not the client.Most common: JP Morgan Chase · JPMorgan Chase & Co. - Home Office · CNH IndustrialFiles as an H-1B dependent employer.

Files H-1B transfers

28 transfer filings in the last year, covering 28 workers. Median labor-condition decision: 8 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • We are looking for an experienced Data Engineer with strong Google Cloud Platform (Google Cloud Platform) expertise to design, develop, and maintain scalable data pipelines and data-processing solutions.
  • The ideal candidate will have strong hands-on experience with Python, SQL, BigQuery, Dataproc, Pub/Sub, Apache Airflow, and DAG development, along with a solid understanding of cloud-based data engineering and ETL/ELT architectures.

Responsibilities

  • Design, develop, and maintain scalable data pipelines and ETL/ELT workflows on Google Cloud Platform.
  • Develop data-processing solutions using Python and SQL.
  • Build and optimize data models, queries, and large-scale datasets in Google BigQuery.
  • Develop distributed data-processing workloads using Google Cloud Platform Dataproc and related technologies.
  • Build event-driven and real-time data pipelines using Google Cloud Pub/Sub.
  • Develop, schedule, and maintain workflows using Apache Airflow and DAGs.
  • Optimize BigQuery queries and data-processing workloads for performance and cost.
  • Implement appropriate data validation, logging, monitoring, and error-handling mechanisms.
  • Collaborate with data architects, analysts, application teams, and business stakeholders to understand data requirements.

Requirements

  • Experience working with large-volume datasets
  • Experience with PySpark / Apache Spark
  • Required Technical Skills

Nice to have

  • Monitor data pipelines and troubleshoot performance, reliability, and data-quality issues.
  • Integrate data from multiple structured and unstructured sources.
  • Follow engineering best practices for source control, testing, CI/CD, security, and production deployment.
  • Strong hands-on Python development experience
  • Advanced SQL skills
  • Strong hands-on experience with Google Cloud Platform (Google Cloud Platform)
  • BigQuery data modeling, complex SQL, performance optimization, and large-scale data processing
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