ETL Developer

Role Overview

As a Development Lead, you will drive the end-to-end lifecycle of data ingestion, transformation, and distribution. You will provide onsite presence and technical leadership to offshore team, enforce best practices for code quality and testing, collaborate with enterprise architects to align data pipelines with critical business intelligence and analytics requirements. You will also be working hands-on developing critical data pipeline artefacts. This role requires working from Client location in Orlando from Mon-Thu and can work remotely on Fri.

Key Responsibilities

Develop data/ETL pipelines and assess review the artefacts generated by a team of Data Engineers working from Offshore. Conduct code reviews, facilitate agile ceremonies, and oversee project delivery timelines.

Design scalable, robust, and fault-tolerant batch and real-time data ingestion pipelines. Establish coding standards and integration design patterns.

Oversee the Extraction, Transformation, and Loading (ETL) or Streaming Data processes across heterogeneous source systems (e.g., APIs, relational databases, flat files, and cloud storage) to centralized data warehouses and data lakes.

Implement data profiling, Data reconciliation processes, validation frameworks, and automated testing procedures to ensure data integrity, consistency, and compliance.

Partner with data architects, Data admins, product owners, and business analysts to translate complex business requirements into technical specifications.

Troubleshoot critical data pipeline bottlenecks, optimize SQL queries, and manage incident resolution.

Requirements

Bachelor’s degree in computer science, Information Technology, or a related field.; 6+ years of experience in data warehousing, data engineering, or ETL/ELT development, with at least 1–2 years in a technical lead or senior engineering capacity.; Advanced proficiency in SQL and scripting languages like Python or Java.; Hands-on experience with modern data integration platforms (e.g., AWS Glue, Apache Spark, or Kafka).; Deep understanding of data modeling (snowflake schemas), real-time streaming architectures, and performance tuning large datasets.; Exceptional communication, stakeholder management, and project management capabilities.