Lead AWS Data Engineer

SFE; Owings Mills (MD); On-site; USD 120,000 - 170,000; Full time

Job summary

SFE is seeking an experienced Data Engineer to design and maintain scalable data lakes, data marts, and data meshes. You will drive data integration using Python, AWS Data Services, DBT, and Apache Airflow, ensuring high data quality and performance in a collaborative, agile environment.

Ideal candidates bring 12+ years of hands-on experience with SQL, data warehousing lifecycles, and cloud ETL/ELT strategies, plus strong stakeholder communication and mentorship skills.

Qualifications

• 12+ years of hands-on experience in Python, AWS Data Services, DBT, Airflow, SQL and PySpark.
• Strong knowledge of SQL and Data Warehousing lifecycle.
• Experience in building data pipelines and orchestrating with DBT & Apache Airflow.
• Experience with data migrations and development of Operational Data Stores, Enterprise Data Warehouses, Data Lake and Data Marts.
• Experience in fixing performance issues / parallelism and data model exposure.
• Cloud ETL/ELT experience with tools like Glue/EMR or similar is a plus.
• Experience with Snowflake, DB2, Postgres and other DBs is advantageous.
• Excellent communication to liaise with Business & IT stakeholders.
• Expertise in planning and estimating project efforts.
• Exposure to Agile ways of working.

Responsibilities

• Lead the design of reliable, secure, and highly available data lakes, data marts and data meshes.
• Lead the design, development, and maintenance of data integration solutions using Python, AWS Data Services, DBT ensuring data pipeline & data quality.
• Collaborate with business stakeholders and architects to define enterprise data strategy, data models, and migration roadmap.
• Translate complex technical constraints into business insights and manage expectations across cross-functional teams.
• Develop, modify, configure & debug existing data pipelines per business requirements.
• Troubleshoot and resolve technical issues; debug, tune and optimize code for performance.
• Manage new requirements, review existing jobs, perform gap analyses & fix performance issues.
• Guide, coach, and upskill junior/mid-level data engineers on best practices.
• Document all data flows, mappings, sessions and workflows.
• Ticket handling and problem ticket analysis in Agile / POD approach.

Skills

Python

AWS Data Services

DBT

Apache Airflow

SQL

PySpark

Data Warehousing

Data orchestration

Tools

Snowflake

DB2

Postgres

Job description

Must Have Technical/Functional Skills

• 12+ years of solid hands-on experience in Python, AWS Data Services, DBT, Apache Airflow (on Astronomer platform), SQL and PySpark
• Very Good hands-on knowledge on SQL and Data Warehousing life cycle is an absolute requirement.
• Experience in creating data pipelines and orchestrating using DBT & Apache Airflow
• Significant experience with data migrations and development of Operational Data Stores, Enterprise Data Warehouses, Data Lake and Data Marts.
• Experiencing in fixing performance issues / parallelism, Data model exposure is a must.
• Good to have: Experience with cloud ETL and ELT in one of the tools like Glue/EMR or any other ELT tool
• Experience in using Snowflake, DB2, Postgres and other database technologies is plus.
• Excellent communication skills to liaise with Business & IT stakeholders.
• Expertise in planning execution of a project and efforts estimation.
• Exposure to working in Agile ways of working

Roles & Responsibilities

• Lead the design of reliable, secure, and highly available data lakes, data marts and data meshes
• leads the design, development, and maintenance of data integration solutions using Python, AWS Data Services, DBT ensuring data pipeline & data quality
• Collaborate with business stakeholders and architects to define the enterprise data strategy, data models, and migration Roadmap
• Translate complex technical constraints into business insights and manage expectations across cross-functional teams
• Develop, modify, configure & debug existing data pipeline as per the business requirement.
• Troubleshoot and resolve technical issues. Debug, tune and optimize code for optimal performance
• Manage the new requirements, Review the existing jobs, Perform gap analysis & Fixing performance issues, etc.
• Guide, coach, and upskill junior and mid-level data engineers on best practices, coding standards, and modern data patterns
• Document all data flow & mappings, sessions and workflows
• Ticket handling and problem ticket analysis skills in Agile /POD approach