Senior Data Engineer

The Opportunity

As a Senior Data Engineer at Orijin, you will be a technical leader responsible for building, scaling, and modernizing the company’s data platform. Your primary focus will be on data modeling, pipelines, architecture, reliability, and performance, ensuring that data is trusted, timely, and production-ready.
 
You will partner closely with data analysts, engineers and product managers to shape how data is modeled and used. You will bring an analytical mindset to pipeline design and enable high-quality insights across the organization. You will ensure company-wide confidence in data quality and enable data-enabled differentiating products and services.

Job Requirements

Data Platform & Architecture Leadership
Design and evolve Orijin’s data architecture to support scalability, reliability, and near–real-time use cases.
Define standards for data modeling, orchestration, versioning, and deployment.
Lead efforts around data governance, security, lineage, and compliance in partnership with stakeholders.
Drive the transition toward modern data stack best practices (event-driven ingestion and streaming where appropriate).

Data Engineering & Pipeline Ownership
Own the design, build, and maintenance of production-grade data pipelines across batch and streaming workloads.
Build systems that support:
Monitoring, alerting, and observability for data pipelines.
 Backfills re-runs, and safe rollbacks when failures or data issues occur.
 High data quality and reliability through automated checks and validation.
Optimize pipelines for performance, cost efficiency, and scalability.
Lead the move toward near real-time data processing where it delivers business value.

Tooling & Infrastructure
Architect and maintain data systems using tools such as:
AWS (S3, RDS, Redshift, Lambda, DMS, Glue etc.)
Data orchestration and ETL tools like Airflow, Airbyte and dbt
Improve CI/CD for data workflows, including testing, deployment, and environment management.
Evaluate and introduce new tooling for orchestration, monitoring, and data quality as the platform matures.

ML & AI Enablement 
Design build, and operate data and feature pipelines that support machine learning and AI-driven product features, including training, evaluation, inference, monitoring, and safe rollout to downstream systems.
Support vectorization and embedding workflows, including generation, storage, refresh, and backfill of embeddings.
Partner with team stakeholders to translate model requirements into scalable, reliable data systems.
Contribute to early experimentation and prototyping of ML-powered features. 

Analytics Enablement & Collaboration
Partner with analysts and product teams to ensure pipelines and data models support meaningful analysis and reporting.
Provide architectural input on metrics design, data models, and semantic layers.
Enable self-service analytics by ensuring clean, well-documented, and accessible datasets.
Basic proficiency in data visualization platforms with demonstrated ability to build and maintain data dashboards
Contribute to exploratory analysis or metric definition when deeper engineering context is required.

Efficiency & Reliability Focus
Continuously improve:
Query performance
Storage and compute costs
Pipeline runtime and failure rates
Lead incident response for data outages and quality issues, including root-cause analysis and permanent fixes.
Establish SLAs and reliability standards for critical data assets.

Qualifications

Bachelor’s or advanced degree in Computer Science, Engineering, Data Science, or equivalent work experience.
Expertise in the areas of data engineering, platform engineering, or backend engineering roles.
Proven experience designing and operating large-scale data pipelines and data platforms in production enviroments.
Strong proficiency in Python and SQL for data engineering workflows.
Hands-on experience with AWS data tools like Redshift, Lambda and Glue or equivalents; experience with data orchestration and ETL tools like Airflow, Airbyte and dbt in production enviroments.
Experience implementing monitoring, alerting, and data quality frameworks.
Familarity with streaming or near–real-time systems (e.g., Kafka, Kinesis, or similar) is a plus.
Hands on experience with PostgreSQL databases and NoSQL style databases like MongoDB, DynamoDB, etc.
Experience supporting machine learning or AI workflows (e.g., feature engineering, embedding pipelines, model inputs/outputs, embeddings, vector databases).
Strong collaboration and communication skills - able to translate business and analytical needs into robust technical systems.
Experience with data governance, security, and compliance in regulated or sensitive-data environments.