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Work setup in the employer’s posting: TELECOMMUTEFrom the employer’s posting
Review the existing data ingestion and transformation pipelines built by the current vendor
Review architecture documentation and source code, using accelerators to speed up the review
Deliver analysis outcomes and complexity evaluations to guide future direction
Shape solution design and calculate effort estimates for the target-state build-out with the discovery team
Examine the existing setup of data ingestion and transformation pipelines built by the current vendor
Dig into architecture documentation and underlying source code, applying accelerators to make the code review process more efficient
Deliver well-defined analysis outcomes and complexity evaluations that shape decisions around future direction
Work hand-in-hand with the discovery team on shaping solution design and calculating effort estimates for the target state build-out
At least 7 years of applicable background in Data Integration
A minimum of two years spent guiding and managing teams
Solid grasp of ETL/ELT workflows, data warehousing concepts, and cloud-native data integration methods
Background working with the AWS/Serverless ecosystem, Snowflake, dbt, and comparable modern data stack tools
Direct experience using AWS Glue to construct and manage data integration pipelines
We're on the search for a highly skilled Chief Data Integration Engineer to come aboard our team. This position provides a chance to engage with intricate, large-scale data infrastructures that underpin essential business functions. You'll combine deep technical knowledge with leadership background to drive impactful changes across the data ecosystem. This opportunity suits someone who thrives on blending detailed technical evaluation with high-level strategic thinking and collaborative teamwork Responsibilities Examine the existing setup of data ingestion and transformation pipelines built by the current vendor to gauge their design and effectiveness Dig into architecture documentation and underlying source code, applying accelerators to make the code review process more efficient Deliver well-defined analysis outcomes and complexity evaluations that shape decisions around future direction Work hand-in-hand with the discovery team on shaping solution design and calculating effort estimates for the target state build-out Requirements At least 7 years of applicable background in Data Integration A minimum of two years spent guiding and managing teams Solid grasp of ETL/ELT workflows, data warehousing concepts, and cloud-native data integration methods Background working with the AWS/Serverless ecosystem, Snowflake, dbt, and comparable modern data stack tools Direct experience using AWS Glue to construct and manage data integration pipelines Strong command of Python for creating and sustaining data integration solutions Deep knowledge of building scalable data architectures and cloud-native transformation workflows Advanced capability in SQL and scripting languages for manipulating data and automating tasks Strong problem-solving aptitude paired with fluent English communication at a B2 level or above Nice to have Background using Dagster to orchestrate and oversee data pipeline operations
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We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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