Experienced Lead Data Engineer

We're looking for a highly experienced Lead Data Engineer / Data Scientist to join our team on a full-time, long-term basis. You'll own the design and delivery of production-grade data platforms and machine learning solutions, working closely with cross-functional and client-facing stakeholders. This is a hands-on leadership role you'll be building pipelines and models yourself, not just directing others. You should be comfortable working independently, setting technical direction, and communicating complex technical concepts clearly to non-technical stakeholders in client-facing conversations. Responsibility - Design, build, and maintain scalable data pipelines and warehouse/lakehouse architecture - Lead technical decisions on data platform architecture and tooling - Build, evaluate, and productionize machine learning models where needed - Partner directly with clients/stakeholders to translate business questions into technical solutions - Establish data engineering best practices and review the work of other team members as the team grows - Own data quality, pipeline reliability, and cost optimization across cloud infrastructure Requirements - 10+ years of professional software/data engineering experience, with strong hands-on expertise in modern data tooling - Strong SQL and at least one major programming language (Python required; Scala/Java a plus) - Experience designing and orchestrating ETL/ELT workflows (Airflow, dbt, Dagster, or similar) - Data warehousing expertise on at least one of: AWS (Redshift, Glue, S3, Athena), GCP (BigQuery, Dataflow, Cloud Composer), or Azure (Synapse, Data Factory, ADLS) - Experience with distributed processing frameworks (Spark, Databricks, or equivalent) - Solid understanding of data modeling (star/snowflake schemas, dimensional modeling) and warehouse/lakehouse architecture - Familiarity with streaming/event systems (Kafka, Kinesis, Pub/Sub) for real-time data use cases - CI/CD for data pipelines and strong version control discipline (Git) - Experience with containerization (Docker; Kubernetes a plus) Comfortable with the standard Python data science stack (pandas, NumPy, scikit-learn) - Experience translating business questions into analytical/statistical approaches - Ability to build and evaluate ML models and productionize them (not just notebook work) - Understanding of experiment design, A/B testing, or statistical inference is a plus - Infrastructure-as-code experience (Terraform, CloudFormation) is a plus Preferred Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. - Native or Bilingual English proficiency, not just fluency. - Prior hands-on experience in Data Engineering and team leadership Answer the screening questions: - A loom video with a short intro introduction - Your updated resume. - Your availability during Eastern Time. - Links to portfolio, or technical publications (if available). Thank you