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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining scalable data pipelines using AWS technologies, including AWS Glue and S3, while ensuring data quality and compliance with enterprise standards. Proficient in developing ETL/ELT solutions and collaborating with cross-functional teams to optimize data processing and architecture.
Highest-signal resume keywords
AWS GluePySpark ProgrammingETL/ELT SolutionsData ModelingTerraform
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringAWS Step FunctionsApache AirflowSQL DevelopmentApache IcebergData WarehousingCloudWatch MonitoringData Quality ManagementInfrastructure as CodePerformance Tuning
Soft Skills
CollaborationMentoringProblem SolvingCommunicationContinuous Improvement
Tools & Technologies
Amazon S3AWS GlueAthenaMWAA (Apache Airflow)TerraformCloudWatchVPCCI/CD PipelinesData LakesMedallion Architecture
Certifications & Qualifications
AWS Certification
Industry Keywords
Wealth ManagementAsset ManagementCapital MarketsInvestment BankingFinancial ServicesRegulatory ComplianceRisk ReportingTrade ProcessingClient ReportingSecurities Data
Tech Stack
Tools & technologiesAirflowApacheAWSCloudETLOraclePySparkPythonSparkSQLTerraform
About the role
Key responsibilities & impact- Design, develop, and maintain scalable data pipelines using AWS Glue (PySpark), Amazon S3, AWS Step Functions, and Athena
- Build robust ETL/ELT solutions for batch and near real-time data processing
- Develop reusable PySpark frameworks and data transformation components
- Work with architects and business stakeholders to implement data platform requirements
- Participate in migration and modernization initiatives from on-premises data platforms to AWS cloud environments
- Implement and support enterprise data lakes using Medallion Architecture
- Develop and maintain Apache Iceberg tables for storage, schema evolution, and incremental processing
- Ensure data quality, lineage, reconciliation, and auditability across the data platform
- Contribute to data modeling and optimization for analytical workloads
- Develop and maintain Apache Airflow (MWAA) workflows and DAGs
- Automate data movement, validation, monitoring, and notification processes
- Implement retry mechanisms, dependency management, and failure handling within workflows
- Integrate Airflow with AWS Glue, S3, Athena, and downstream applications
- Implement AWS security best practices, including IAM roles, KMS encryption, and secrets management
- Support infrastructure provisioning and deployment using Terraform
- Collaborate with DevOps, infrastructure, and security teams to maintain secure and reliable cloud environments
- Assist in configuring VPC endpoints, networking connectivity, and service integrations
- Monitor and troubleshoot production data pipelines and workflows
- Perform performance tuning of AWS Glue jobs and Spark workloads
- Implement logging, monitoring, and alerting using Amazon CloudWatch
- Participate in incident resolution, root cause analysis, and continuous improvement initiatives
- Ensure adherence to enterprise operational and governance standards
- Collaborate with data architects, analysts, application teams, and business users
- Participate in code reviews, design discussions, and technical documentation
- Mentor junior engineers and share AWS data engineering best practices
- Stay current with emerging AWS services and modern data engineering trends
Requirements
What you’ll need- Bachelor's degree in Computer Science, Engineering, Information Technology, or related discipline
- 5-8 years of experience in Data Engineering with strong AWS exposure
- Hands-on experience with AWS Glue, Amazon S3, Athena, MWAA (Apache Airflow), Step Functions, and CloudWatch
- Strong programming skills in Python and PySpark
- Experience building large-scale ETL/ELT data pipelines
- Good understanding of data modelling, data warehousing, and distributed processing concepts
- Experience with Apache Iceberg and cloud-native data lake architectures
- Experience using Infrastructure as Code tools such as Terraform
- Strong SQL development and query optimization skills
- Experience supporting production-grade data platforms and troubleshooting complex issues
- Good understanding of AI technologies, agentic systems, Microsoft Copilot, and Claude
- Experience in Wealth Management, Asset Management, Capital Markets, Investment Banking, or Financial Services highly preferred
- Experience with portfolio and investment management, securities and holdings data, trade processing, risk and compliance reporting, regulatory reporting, market and reference data, advisor compensation and payout platforms, or client reporting solutions
- Experience with Oracle databases or ODI migrations is nice to have
- Knowledge of CI/CD pipelines and DevOps practices is nice to have
- Experience with data quality and metadata management tools is nice to have
- Familiarity with enterprise governance and regulatory compliance requirements is nice to have
- AWS Certification is nice to have
Benefits
Comp & perks- Flexible environment
- Health and wellness packages
- Rewards
- Learning opportunities
- Professional development opportunities
- Opportunities to build new skills
- Leadership opportunities
- Mentorship and career growth
- Diversity, equity and inclusion culture
- Disability-related adjustments or accommodations during recruitment