ETL Architect
2 days ago
We are seeking an experienced ETL Tech Lead to oversee and drive data migration, integration, and pipeline development projects. This role involves leading the migration of on-premise databases (Oracle, SQL Server) to AWS cloud platforms such as Redshift and S3, ensuring seamless integration across diverse systems. The ideal candidate will design and optimize robust ETL/ELT workflows, leveraging tools like Informatica, AWS Glue, and custom Python solutions, while maintaining data quality and governance. Additionally, the role requires expertise in building scalable data architectures using AWS services, managing automated workflows with Apache Airflow, and mentoring junior engineers. A strong background in SQL, Python, and AWS cloud infrastructure is essential, with a focus on driving high-performance data operations aligned with business objectives.
About the Role
Data Migration & Integration: Lead data migration projects to move on-premise data (Oracle, SQL Server) to AWS cloud-based solutions (Redshift, S3, RDS). Coordinate end-to-end processes for seamless data transfers and integrations across platforms.
Data Ingestion & Pipelines: Design, implement, and maintain robust, scalable data ingestion pipelines to integrate structured and unstructured data from various sources into cloud-based platforms like AWS. Use tools like Informatica, Apache Airflow, or custom Python solutions.
Cloud Infrastructure (AWS): Develop cloud-native data architectures using AWS services (e.g., Redshift, S3, Glue, Lambda, RDS, and Athena). Optimize data storage, processing, and retrieval in AWS to meet performance and cost efficiency goals.
ETL/ELT Development: Build and optimize ETL/ELT processes using tools like Informatica, AWS Glue, AWS Transfer Family and custom Python-based solutions. Ensure data flows are automated, efficient, and scalable.
Automation & Workflow Orchestration: Implement and manage workflow orchestration with Apache Airflow to automate and schedule ETL tasks, ensuring reliable data delivery to target systems.
Collaborate with Stakeholders: Work closely with business users, analysts, and other engineering teams to understand data requirements, propose data solutions, and ensure alignment with business goals. Translate business needs into technical solutions.
Data Quality & Governance: Ensure data quality, integrity, and compliance with governance policies. Implement monitoring and logging to ensure pipeline health and detect anomalies or issues proactively.
Mentoring & Leadership: Mentor and guide junior data engineers, helping them grow in their technical skills and best practices. Promote a culture of continuous learning and high performance.
Performance Tuning & Optimization: Continuously monitor and optimize data pipeline performance, troubleshoot issues, and apply best practices for improving query performance and data processing times.
Skills (Must have) :
Experience with Data Migrations: Proven track record of handling large-scale data migrations, particularly from on-premise databases (e.g., Oracle, SQL Server) to cloud platforms like AWS.
Cloud Platforms (AWS): Strong hands-on experience with AWS services including S3, Redshift, RDS, Lambda, Glue, and Athena. Familiarity with AWs Transfer Family, AWS Data Pipeline and AWS DMS (Database Migration Service) is a plus.
ETL/ELT Tools: Extensive experience with ETL/ELT tools like Informatica, AWS Glue, or Apache NiFi, as well as custom Python scripts for data transformations and data workflows.
Python Programming: Advanced proficiency in Python, including experience with libraries such as Pandas, NumPy, and PySpark for data processing and transformations. Experience in writing efficient, scalable code is essential.
Apache Airflow: Proficiency in Apache Airflow for building, scheduling, and managing data workflows and pipeline automation.
SQL & Database Skills: Strong experience with relational databases like Oracle, SQL Server, and cloud databases like Amazon Redshift or RDS. Familiarity with writing complex SQL queries and optimizing them for large-scale data processing.
Data Modeling & Warehousing: Solid understanding of data modeling techniques (e.g., star schema, snowflake schema) and experience with data warehousing solutions, particularly in the cloud (AWS Redshift, Snowflake, etc.).
Data Integration: Experience integrating data from various sources (databases, APIs, flat files, etc.), and managing large-scale data ingestion pipelines.
Data Quality & Governance: Experience implementing data quality checks, error handling, and monitoring for data pipelines. Familiarity with data governance best practices for compliance and security.
Version Control & CI/CD: Experience with version control (e.g., Git) and CI/CD practices for managing code and automating data workflows. Familiarity with Jenkins, GitLab CI, or other tools is a plus.
Skills (Good to Have) :
Financial Services/Capital Markets Experience: Experience working with financial data, including market data, transaction data, risk data, or financial reporting and working with financial data vendors such as Bloomberg, Refinitiv, Morningstar, Barclays.
Big Data Technologies: Experience with Big Data tools and frameworks like Hadoop, Spark, or Kafka is a plus, particularly in a cloud-based environment (AWS EMR, AWS Kinesis, etc.).
Data Security & Compliance: Knowledge of best practices for securing data, particularly in financial services, including encryption, data masking, and managing sensitive information in cloud environments.
Leadership Experience: For Lead roles, experience managing or mentoring a team of data engineers, guiding architecture decisions, and driving process improvements.
Education and Certifications:
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience).
Certifications in AWS (e.g., AWS Certified Data Analytics – Specialty, AWS Certified Solutions Architect – Associate/Professional) are highly desirable.
Additional certifications in data engineering, databases, or cloud platforms would be beneficial.
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