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Data Engineer
GFT Technologies. Organize, collect, and process large volumes of data using ETL tools .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data extraction, transformation, and loading (ETL) processes, utilizing tools such as Python and AWS services. Proficient in managing data pipelines, ensuring data quality, and implementing dimensional modeling for effective business intelligence.
Highest-signal resume keywords
ETL Tools ExperiencePython/PySpark ProficiencyAWS Cloud Services ExpertiseDimensional Modeling SkillsData Quality Management
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 ExtractionData TransformationData LoadingDimensional ModelingQuery OptimizationData Quality AssuranceData Lifecycle ManagementLakehouse ArchitectureNoSQL DatabasesInfrastructure-as-Code
Soft Skills
Strong Communication SkillsCollaborative MindsetResilienceResults Orientation
Tools & Technologies
AWS GlueAWS EMRAWS AthenaAWS LambdaMongoDBDynamoDBApache IcebergTerraformCloudWatchSpark SQL
Certifications & Qualifications
AWS PractitionerAWS Data Engineer
Industry Keywords
Data QualityData GovernanceData MeshAI Applied to DataCloud Monitoring
Tech Stack
Tools & technologiesApacheAWSCloudDynamoDBETLHadoopMongoDBNoSQLPySparkPythonSparkSQLTerraform
About the role
Key responsibilities & impact- Organize, collect, and process large volumes of data using ETL tools
- Translate the client's business objectives into an information management and business intelligence strategy
- Maintain data pipelines and ensure secure access to information
- Create and expose metrics and dashboards based on the data collected
- Ensure data quality and integrity
- Contribute to data platform modernization initiatives
- Work with Lakehouse architecture, governance, quality, and data preparation for analytical and AI consumption
- Design dimensional models for consumption by business teams, dashboards, and reports
- Manage the complete data lifecycle: ingestion, processing, storage, transformation, consumption, and governance
Requirements
What you’ll need- Experience in data extraction, transformation, and loading (ETL tools)
- Python/PySpark
- AWS Cloud: Glue, EMR, Athena, SNS, Lambda, Step Functions, S3, Lake Formation, IAM, and CloudWatch
- NoSQL modeling and databases: MongoDB, DynamoDB, and Hadoop
- Lakehouse architecture with Apache Iceberg
- Query engines for large volumes of data: Athena, Trino, Presto, or Spark SQL
- Ability to optimize queries and work with columnar formats (Parquet, ORC)
- Dimensional modeling
- Experience with structured, semi-structured, and unstructured data
- Execution of unit and performance tests
- Best practices in security, access control, and cloud monitoring
- AWS Practitioner and AWS Data Engineer certifications are desirable
- Experience with data quality and observability is desirable
- Experience with infrastructure-as-code tools (Terraform, CloudWatch) is desirable
- Knowledge of Data Mesh is desirable
- Knowledge of AI applied to data is desirable
- Hands-on approach, collaborative mindset, resilience, and results orientation
- Strong communication skills and ability to work effectively in a team
Benefits
Comp & perks- Flexible benefits card – choose how and where to use it.
- Scholarships for undergraduate, graduate, MBA, and language courses.
- Certification incentive programs.
- Flexible working hours.
- Competitive salaries.
- Annual performance review with a structured career development plan.
- International career opportunities.
- Wellhub and TotalPass.
- Private pension plan.
- Childcare assistance.
- Health insurance.
- Dental insurance.
- Life insurance.