About the Role:
We are seeking a seasoned and hands-on Lead / Principal Data Engineer to spearhead our data engineering efforts. You will be responsible for architecting, building, and scaling robust data pipelines and systems that empower our product, analytics, and machine learning teams. This role is both strategic and technical, requiring deep expertise in modern data platforms and a strong product mindset.
Key Responsibilities:
- Design, build, and maintain scalable data pipelines and ETL processes to ingest data from a variety of sources.
- Define and enforce best practices for data modeling, quality, governance, and security.
- Lead architectural decisions related to data infrastructure and collaborate closely with product, analytics, and engineering teams.
- Own and optimize our data lake/warehouse infrastructure (e.g., Snowflake, BigQuery, Redshift).
- Guide and mentor a team of data engineers; lead code reviews and improve engineering excellence.
- Implement real-time data streaming solutions (e.g., Kafka, Spark Streaming).
- Enable self-serve analytics and data democratization across the organization.
- Collaborate with data science and analytics teams to operationalize ML models and dashboards.
- Monitor and improve data system performance, reliability, and scalability.
Key Requirements:
- 8–12 years of experience in data engineering or related roles.
- Strong programming skills in Python, Scala, or Java.
- Deep expertise in SQL and performance tuning.
- Hands-on experience with modern data stack tools such as Airflow, dbt, Kafka, Spark, or Flink.
- Experience with data lake and data warehouse technologies like Snowflake, BigQuery, Redshift, Delta Lake.
- Experience with cloud platforms (AWS / GCP / Azure) and orchestration tools (e.g., Airflow, Dagster).
- Strong understanding of data privacy, security, and compliance (GDPR, HIPAA, etc.).
- Excellent communication and leadership skills; ability to influence cross-functional teams.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Nice to Have:
- Experience in building data platforms in high-scale environments.
- Familiarity with ML pipelines and MLOps.
- Contributions to open-source data tools or frameworks.
- Experience in a domain such as fintech is a plus.
Work Location: In person
