Data Engineer

Data Engineer

About the Role

We are seeking a highly skilled
Data Engineer

to design, build, and optimize scalable data infrastructure that powers analytics, product insights, and business decision-making. This role requires strong expertise in cloud-native data systems, distributed processing, and building reliable, high-performance data pipelines.

You will play a critical role in enabling data-driven growth by ensuring data is accurate, accessible, and actionable across the organization.

Must Have

- 5+ years of experience in
data engineering

within cloud-native environments
- Strong expertise in
Python, SQL, and data processing frameworks
- Hands-on experience with
AWS (S3, Glue, Kinesis, Athena/Presto, Redshift)
- Experience building and maintaining
scalable ETL/ELT pipelines and data lakes
- Solid understanding of
distributed systems (e.g., Spark)
- Experience with
Docker and containerized data workflows
- Strong knowledge of
data quality, validation, and monitoring practices
- Proven ability to collaborate with
BI, analytics, and engineering teams

Experience

- 5+ years of experience in
data engineering within AWS-based environments
- Experience supporting
data-driven decision-making across product, marketing, or operations teams

Your Role

- Design, build, and maintain
scalable data pipelines and data lake architectures
- Lead the development of
robust ETL/ELT processes

for structured and unstructured data
- Partner with
analytics and BI teams

to deliver high-quality, query-ready datasets
- Ensure
data integrity, availability, and performance

across all systems
- Collaborate with cross-functional teams to translate
business requirements into data solutions
- Optimize data workflows for
efficiency, scalability, and cost-effectiveness

Outcomes

- Build and optimize
reliable, scalable ETL pipelines

that proactively detect and resolve data issues
- Own
data ingestion, transformation, and modeling

across multiple data sources
- Deliver
clean, well-structured datasets

that drive business insights and growth
- Implement and maintain
data monitoring, anomaly detection, and quality frameworks
- Enable scalable and performant
BI and reporting infrastructure
- Contribute to a
high-performance data engineering culture

through best practices and mentorship