Data Engineer - Google Cloud Platform BigQuery (Data Warehouse Migration)

STEPS, INC

United States

4 days ago

Role details

Contract type

Temporary contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

4 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Airflow Amazon Web Services Microsoft Azure Big Data BigQuery Cloud Computing Cloud Storage Cluster Analysis Data Validation Information Engineering Extract Transform Load (ETL) Data Migration

+37 more

Data Profiling Data Warehousing DevOps Data Flow Control Apache Hadoop Apache Hive Python (Programming Language) Microsoft SQL Server Oracle (Applications) Performance Tuning Query Optimization Cloud Services Cloudera SQL Databases Data Streaming Teradata SQL Workflow Management Systems Data Processing Google Cloud Azure Data Factory Netezza Snowflake Apache Spark Indexer Git Data Lakes Pyspark Data Lineage Data Management Azure Synapse Analytics Software Version Control Data Pipelines Apache Beam Amazon Elastic Mapreduce (EMR) Amazon Redshift Databricks Control M

Requirements

Looking for Data Migration / Data Warehouse Engineers with experience moving legacy data platforms such as Teradata, Hadoop, Databricks, Oracle, SQL Server, AWS, or Azure into Google Cloud Platform BigQuery.

BigQuery experience is needed with any of Databricks, Snowflake migration

Skills/Experience Needed:

6+ years in data engineering, ETL/ELT, analytics engineering, data warehouse development, or big data pipelines;
4 years on Google Cloud Platform cloud data platforms preferred.

Cloud & Data Warehouse

Hands-on experience with BigQuery or another enterprise cloud data warehouse.
Experience working with one or more legacy data platforms such as Teradata, Hadoop/Hive, Databricks, Oracle, SQL Server, Netezza, Redshift, Snowflake, or Azure Synapse.

Data Migration & Data Warehousing

Experience in migration assessment, schema mapping, data profiling, and migration factory approaches.
Strong understanding of dimensional modelling, fact and dimension table design, and SCD Type 1 & Type 2.
Experience with schema evolution, historical data handling, data validation, reconciliation, parallel run, cutover, and rollback strategies.

Data Engineering

Strong proficiency in SQL and Python.
Experience designing, developing, and supporting ETL/ELT pipelines.
Strong understanding of data modelling, data warehousing concepts, partitioning, clustering, indexing, and query optimization.
Experience with cloud storage and data lake architectures.

Big Data & Pipeline Processing

Hands-on experience with batch and streaming data pipelines.
Experience with one or more of the following technologies: Spark / PySpark, Apache Beam, Google Cloud Dataflow, Dataproc, Databricks, Amazon EMR

Workflow Orchestration & DevOps

Experience with orchestration tools such as Apache Airflow, Cloud Composer, Control-M, Azure Data Factory (ADF), dbt, or equivalent.
Experience with CI/CD pipelines and Git-based code versioning.

Data Quality & Performance

Experience implementing data quality checks, schema validation, data lineage, monitoring, and alerting.
Ability to optimize data pipelines and warehouse performance.
Ability to explain real-world architecture, scalability, and performance optimization scenarios-not just tool definitions.