Python Application Engineer, Investor Data Sync Platform

Remote.AR
Remote.Brazil
Remote.Bulgaria
Remote.Colombia
Remote.Poland
Rosario
Белград
Варна
Варшава
Вроцлав
Клуж-Напока
Краков
Ларнака
Лодз
Люблин
Монтевидео
Монтерей
Рига
София

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Project overview

You will work on a data integration platform that orchestrates investor data flows across cloud systems. The platform uses serverless architecture to coordinate data pipelines between Snowflake, Salesforce, and API driven services. The focus is on reliable execution of complex workflows across development, QA, and production environments.

Team

You will collaborate with Python engineers, data engineers, and cloud specialists who are responsible for designing, implementing, and supporting serverless data solutions. The team works in an iterative delivery model, reviews each other’s code, and shares ownership of pipeline quality, observability, and performance.

Position overview

We are looking for a Python Application Engineer who will design, build, and maintain serverless applications that orchestrate data pipelines. You will architect multi step workflows, optimize data transformations, and make sure that pipelines run reliably across development, QA, and production environments.

Technology stack

Python, AWS Lambda, AWS Step Functions, PyArrow, Snowflake, Terraform, GitHub Actions, DynamoDB, Amazon CloudWatch, DataDog, AWS X Ray, REST APIs, Salesforce, Anduin IDM, Amazon S3, Parquet, SQL

Responsibilities

Design and implement serverless Lambda functions in Python with type hints and structured logging
Build AWS Step Functions state machines for complex multi step data workflows including error handling and retry logic
Extract data from Snowflake and transform it using PyArrow with attention to memory efficiency
Integrate with external REST APIs including Salesforce and other platforms and manage authentication tokens
Apply SQL optimization techniques for Snowflake performance tuning
Implement correlation tracking and end to end observability across pipeline executions
Troubleshoot pipeline issues using Amazon CloudWatch logs and DataDog dashboards
Maintain shared Python utilities in Lambda Layers to improve code reusability
Participate in code reviews with a focus on Python best practices and clear design
Deploy infrastructure changes using GitHub Actions with tag based promotion between environments

Requirements

More than five years of Python engineering experience preferably in serverless or cloud based ecosystems
Ability to write Python code with type hints and apply functional programming patterns where appropriate
Practical experience building and consuming REST API clients
Advanced SQL skills including complex joins, window functions, common table expressions, and query optimization in Snowflake
Hands on experience with AWS services including Lambda, Step Functions, DynamoDB, S3, IAM, and CloudWatch
Experience using Terraform to define and debug infrastructure as code for AWS resources
Background in designing ETL or ELT pipelines with attention to error handling, retries, idempotency, and state management
Experience working with Git based workflows including CI CD practices, branch strategies, and code review participation
Ability to investigate and debug distributed systems using Amazon CloudWatch and DataDog logs and metrics

Nice to have

Experience with AWS X Ray or similar distributed tracing tools
Experience building integrations with Salesforce or similar SaaS platforms
Experience designing structured logging standards for data pipelines
Experience working with partitioned Parquet files in Amazon S3

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