Data Engineer: AWS & Databricks + Delta Lake

Acuity, Inc.

Reston (VA)

On-site

USD 100,000 - 160,000

Full time

14 days+

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Job summary

Acuity, Inc. is seeking a highly skilled Data Engineer to join our Engineering Team, focusing on AWS cloud-scale data platforms for federal clients.

You will design, build, and optimize Spark-based pipelines and Delta Lake tables to support ingestion, transformation, and analytics across large datasets. You will collaborate with data architects to implement cloud-native solutions on AWS, ensure data governance, and drive modernization initiatives.

Qualifications

2+ years of experience in data engineering and Agile analytics.
2+ years of experience creating software for retrieving, parsing and processing structured and unstructured data.
1 to 2 years experience building scalable ETL and ELT workflows for reporting and analytics.
1 or more years experience building enterprise data engineering solutions in the cloud, with preferred experience with cloud native technologies from AWS and Databricks.
BA or BS degree

Responsibilities

Build and maintain scalable PySpark-based data pipelines in Databricks notebooks to support ingestion, transformation, and enrichment of structured and semi-structured data.
Design and implement Delta Lake tables optimized for ACID compliance, partition pruning, schema enforcement, and query performance across large datasets.
Develop ETL and ELT workflows that integrate multiple source systems into a centralized, query-optimized data warehouse architecture.
Leverage Spark SQL and DataFrame APIs to implement business rules, dimensional joins, and aggregation logic aligned to warehouse modeling best practices.
Collaborate with data architects and engineers to implement cloud-native data solutions on AWS using S3, Glue, RDS, and IAM for secure, scalable storage and access control.
Monitor pipeline health, job execution, and cluster utilization using native Databricks tools and AWS CloudWatch, identifying bottlenecks and optimizing cost-performance tradeoffs.
Deploy and version data engineering assets using Git-integrated development workflows and automate deployment with CI/CD tools such as GitLab or Jenkins.
Conduct technical discovery and mapping of legacy source systems, identifying required transformations and designing end-to-end data flows.
Implement governance practices including metadata tagging, data quality validation, audit logging, and lineage tracking using platform-native features and custom logic.