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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - Google Cloud Platform BigQuery (Data Warehouse Migration) - **Company:** STEPS, INC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** 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, 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 - **Published:** August 7, 2026 - **Apply:** https://www.dice.com/job-detail/65e5aa7a-0156-402b-a8db-ec6700567d48 ## About the Role 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Got AI ideas but no money? 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