Senior Data Engineer

Senior Data Engineer
Remote · LATAM · Full-Time Contract

LATAM (Remote) Full-Time Contract ASAP English Required
ABOUT THE ROLE

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
platform.
Batch Processing

Design & implement batch pipelines for large-scale processing (GBs to
TBs)

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
Real-Time / Streaming

Design & implement real-time pipelines using Apache Flink (Scala
preferred)

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
Integrate with Kafka or Kinesis
CORE RESPONSIBILITIES

Build and maintain data lake / lakehouse architectures on AWS
(S3-based)

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
TECH STACK

Python SQL Scala PySpark Apache Flink Apache Kafka AWS Glue Amazon EMR Amazon S3 AWS Lambda

Amazon Athena Amazon Kinesis Delta Lake / Iceberg
REQUIRED QUALIFICATIONS

5+ years of experience in Data Engineering
Strong skills in Python, SQL, and Scala

Hands-on AWS experience: S3, Glue, Athena, Lambda, EMR,
Kinesis, IAM

Strong batch processing experience with Spark / PySpark at scale

Proven real-time streaming experience with Apache Flink in
production

Solid understanding of batch vs streaming trade-offs and event-driven
architectures

Experience building data lakes or data warehouses in production

Experience integrating external systems via APIs

Distributed systems fundamentals: fault tolerance, partitioning,
consistency
WHAT MAKES A STRONG CANDIDATE

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
NICE TO HAVE

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
Functions)

Background in IoT, telemetry, or event-heavy domains

OpenTelemetry or advanced observability experience
THIS ROLE IS NOT FOR YOU IF

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
WHY COMMITTED STAFF

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