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Committedstaff
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LATAM (Remote) Full-Time Contract ASAP English Required
We are looking for a Senior Data Engineer with deep expertise in AWS, batch processing, and real-time streaming architectures. This is a hands-on
role — you will design and own scalable data pipelines serving both high-throughput batch workloads (GBs to TBs) and low-latency streaming
systems. You will collaborate with analysts, data scientists, and business teams, and help mentor junior engineers to raise the bar across the data
Design & implement batch pipelines for large-scale processing (GBs to
Build ETL/ELT workflows using PySpark, SQL, Glue, and EMR
Optimize jobs for performance, partitioning, and cost
Manage data ingestion from DBs, files, and APIs into the data lake
Ensure data quality, consistency, and reliability
Design & implement real-time pipelines using Apache Flink (Scala
Process high-throughput streams with low-latency requirements
Implement stateful processing, windowing, and event-time handling
Handle late/out-of-order events; exactly-once / at-least-once semantics
Configure checkpointing, fault tolerance, and recovery
Build and maintain data lake / lakehouse architectures on AWS
Integrate multiple data sources — APIs, SaaS platforms, databases
Ensure data governance, security, and access control (IAM)
Implement monitoring, logging, and alerting for all pipelines
Collaborate with analysts, data scientists, and business teams
Mentor junior engineers and enforce best practices
Python SQL Scala PySpark Apache Flink Apache Kafka AWS Glue Amazon EMR Amazon S3 AWS Lambda
Amazon Athena Amazon Kinesis Delta Lake / Iceberg
5+ years of experience in Data Engineering
Hands-on AWS experience: S3, Glue, Athena, Lambda, EMR,
Strong batch processing experience with Spark / PySpark at scale
Proven real-time streaming experience with Apache Flink in
Solid understanding of batch vs streaming trade-offs and event-driven
Experience building data lakes or data warehouses in production
Experience integrating external systems via APIs
Distributed systems fundamentals: fault tolerance, partitioning,
Has built both batch AND real-time pipelines in production — not just one or the other
Knows when to apply each paradigm: batch for cost-efficient large volumes, streaming for low-latency real-time use cases
Experience handling millions of records in batch and high event throughput in streaming systems
Strong debugging and performance optimization skills across distributed data systems
Hands-on mindset — you build things and own them end-to-end, not just design them
AWS certifications (Data Analytics Specialty, Solutions Architect)
Experience with Delta Lake, Apache Iceberg, or open table formats
Familiarity with dbt for transformation workflows
Experience with orchestration tools (Apache Airflow, AWS Step
Background in IoT, telemetry, or event-heavy domains
OpenTelemetry or advanced observability experience
You have only worked with batch OR only with streaming — this role requires both
You need a fully defined architecture and runbook before making engineering decisions
You are not comfortable owning pipelines end-to-end in production without daily oversight
You optimize for theoretical elegance before proving a real cost or reliability problem exists
You are not comfortable communicating directly in English with international teams
Committed Staff connects the top 1% of LATAM talent with high-growth U.S. companies. We don't just place candidates — we build committed teams
that scale. You'll get direct access to a real data engineering challenge with meaningful scope, full-time remote work aligned with U.S. time zones,
competitive LATAM-market compensation, and a growing network of senior tech professionals across Latin America.
Originally posted on Himalayas
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