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TELECOMMUTE Data Engineering Lead

ClifyX, INC
United States
3 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
4 years minimum
Working hours
Regular working hours
Job source
dice.com

Tech stack

Airflow Amazon Web Services Amazon S3 Microsoft Azure BigQuery Data as a Services Information Engineering Extract Transform Load (ETL) Python (Programming Language) Cloud Services SQL Databases Google Cloud
+9 more
Snowflake Apache Spark Containerization Pyspark Git Flow Kubernetes Apache Kafka Docker Databricks

Requirements

  • 4-15 years of professional data engineering experience.
  • Strong Python, SQL, and Spark (PySpark) skills, and/or Kafka.
  • Snowflake (Snowpipe, Tasks, Streams) as a complementary warehouse.
  • Databricks (Delta formats, workflows, cataloging) or equivalent Spark platforms.
  • Hands-on experience building ETL/ELT with Prefect (or Airflow), dbt, Spark, and/or Kafka.
  • Experience onboarding datasets to cloud data platforms (storage, compute, security, governance).
  • Familiarity with Azure/AWS/Google Cloud Platform data services (e.g., S3/ADLSS; Redshift/BigQuery; Glue/ADF).
  • Git-based workflows CI/CD and containerization with Docker (Kubernetes a plus).

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on dice.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

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2:15 min

Empowering domain teams with an open data platform

Sandhya Menon Sandhya Menon · WWC Europe 2026

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

4:32 min

Harnessing Spark with Python using PySpark and Py4J

Ayon Roy · LIVE

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · WWC 2025

3:37 min

Scaling machine learning pipelines from prototypes to petabytes

Julian Joseph · LIVE

3:05 min

Audience questions on AI agents and pipeline vectorization

Joy Joy · WWC 2024

Videos

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31:15
WWC Europe 2026 • about 1 month ago

From Messy Queries to Scalable Systems - How Data Engineering actually works

Sandhya Menon

PySpark - Combining Machine Learning & Big Data thumbnail
43:26
LIVE • almost 6 years ago

PySpark - Combining Machine Learning & Big Data

Ayon Roy

Docker Compose: Rediscovered thumbnail
28:40
Europe 2026 Virtual • about 1 month ago

Docker Compose: Rediscovered

Matthieu Vincent

Data Science in Retail thumbnail
58:21
LIVE • about 3 years ago

Data Science in Retail

Julian Joseph

Enjoying SQL data pipelines with dbt thumbnail
26:53
WWC 2023 • almost 3 years ago

Enjoying SQL data pipelines with dbt

Matthias Niehoff

Empowering Retail Through Applied Machine Learning thumbnail
22:41
WWC 2024 • almost 2 years ago

Empowering Retail Through Applied Machine Learning

Christoph Fassbach, Daniel Rohr

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