Jobs via Dice
Senior AWS Data Engineer
Location: Baltimore, MD
Salary: $190,000 – $240,000
A Senior AWS Data Engineer designs, builds, and maintains scalable data pipelines (ETL/ELT) on AWS using services like S3, Glue, Redshift, Lambda, with Python/PySpark, SQL for complex transformations, orchestrating with tools like , ensuring data quality, security, governance, and performance optimization for analytics/BI, collaborating with stakeholders to translate needs into robust, cloud‑native data solutions.
Key Responsibilities
Build, automate, and monitor scalable ETL/ELT data pipelines using Python, PySpark, Spark SQL, and AWS services (S3, Glue, Lambda, EMR, Redshift).
Design and implement data models, data lakes, data warehouses, and lakehouse architectures for analytics.
Develop and manage workflows using orchestrators like Airflow, EventBridge, or AWS Step Functions.
Tune and optimize data pipelines and queries for speed, cost‑efficiency, and reliability.
Partner with data scientists, analysts, and business stakeholders to gather requirements and deliver data‑driven solutions.
Implement data quality checks, security (encryption, access controls), and governance best practices.
Apply CI/CD, version control (Git), and testing to data workflows.
Core Skills
AWS: S3, Glue, EMR, Redshift, Lambda, Athena, Kinesis, SNS, SQS.
Programming: Advanced Python (data manipulation, APIs), SQL (expert level).
Big Data: Apache Spark, PySpark.
Tools: Airflow, , , (often).
Concepts: Data Warehousing, Data Lake, Data Modeling, Distributed Systems.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Information Technology
Industries Software Development
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Salary: $190,000 – $240,000
A Senior AWS Data Engineer designs, builds, and maintains scalable data pipelines (ETL/ELT) on AWS using services like S3, Glue, Redshift, Lambda, with Python/PySpark, SQL for complex transformations, orchestrating with tools like , ensuring data quality, security, governance, and performance optimization for analytics/BI, collaborating with stakeholders to translate needs into robust, cloud‑native data solutions.
Key Responsibilities
Build, automate, and monitor scalable ETL/ELT data pipelines using Python, PySpark, Spark SQL, and AWS services (S3, Glue, Lambda, EMR, Redshift).
Design and implement data models, data lakes, data warehouses, and lakehouse architectures for analytics.
Develop and manage workflows using orchestrators like Airflow, EventBridge, or AWS Step Functions.
Tune and optimize data pipelines and queries for speed, cost‑efficiency, and reliability.
Partner with data scientists, analysts, and business stakeholders to gather requirements and deliver data‑driven solutions.
Implement data quality checks, security (encryption, access controls), and governance best practices.
Apply CI/CD, version control (Git), and testing to data workflows.
Core Skills
AWS: S3, Glue, EMR, Redshift, Lambda, Athena, Kinesis, SNS, SQS.
Programming: Advanced Python (data manipulation, APIs), SQL (expert level).
Big Data: Apache Spark, PySpark.
Tools: Airflow, , , (often).
Concepts: Data Warehousing, Data Lake, Data Modeling, Distributed Systems.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Information Technology
Industries Software Development
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