Senior Data Engineer – Python & AWS

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Job Overview:

We are looking for an experienced Senior Data Engineer to design, build, and optimize scalable, high-performance data platforms using AWS cloud services and Python. The ideal candidate will play a key role in architecting end-to-end data pipelines, driving automation, ensuring data quality, and enabling analytics and AI workloads across the organization.

This role requires deep technical expertise in AWS data services, modern data architecture, and a passion for delivering reliable, high-quality data solutions at scale.

Key Responsibilities

Architect and implement scalable, fault-tolerant data pipelines using AWS Glue, Lambda, EMR, Step Functions, and Redshift
Build and optimize data lakes and data warehouses on Amazon S3, Redshift, and Athena
Develop Python-based ETL/ELT frameworks and reusable data transformation modules
Integrate multiple data sources (RDBMS, APIs, Kafka/Kinesis, SaaS systems) into unified data models
Lead efforts in data modeling, schema design, and partitioning strategies for performance and cost optimization
Drive data quality, observability, and lineage using AWS Data Catalog, Glue Data Quality, or third-party tools
Define and enforce data governance, security, and compliance best practices (IAM policies, encryption, access control)
Collaborate with cross-functional teams (Data Science, Analytics, Product, DevOps) to support analytical and ML workloads
Implement CI/CD pipelines for data workflows using AWS CodePipeline, GitHub Actions, or Cloud Build
Provide technical leadership, code reviews, and mentoring to junior engineers
Monitor data infrastructure performance, troubleshoot issues, and lead capacity planning

Required Skills & Qualifications

Bachelor’s or Master’s degree in Computer Science, Information Systems, or related field
5–10 years of hands-on experience in data engineering or data platform development
Expert-level proficiency in Python (pandas, PySpark, boto3, SQLAlchemy)

Advanced experience with AWS Data Services, including:

AWS Glue, Lambda, EMR, Step Functions, DynamoDB, EDW Redshift, Athena, S3, Kinesis, Amazon Quicksight.
IAM, CloudWatch, CloudFormation / Terraform (for infrastructure automation)
Strong experience in SQL, data modeling, and performance tuning
Proven ability to design and deploy data lakes, data warehouses, and streaming solutions
Solid understanding of ETL best practices, partitioning, error handling, and data validation
Hands-on experience in version control (Git) and CI/CD for data pipelines
Knowledge of containerization (Docker/Kubernetes) and DevOps concepts
Excellent analytical, debugging, and communication skills

Preferred Skills

Experience with Apache Spark or PySpark on AWS EMR or Glue
Familiarity with Airflow, dbt, or Dagster for workflow orchestration
Exposure to real-time data streaming (Kafka, Kinesis Data Streams, or Firehose)
Knowledge of Lake Formation, Glue Studio, or DataBrew
Experience integrating with machine learning and analytics platforms (SageMaker, QuickSight)
Certification: AWS Certified Data Analytics – Specialty or AWS Certified Solutions Architect

Soft Skills

Strong ownership mindset with focus on reliability and automation
Ability to mentor and guide data engineering teams
Effective communication with both technical and non-technical stakeholders

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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