Position Details
Data Engineering Lead Apply
Tittle: Data Engineering Lead
Location: New york, NY( Remote)
Job Description
Must Have Technical/Functional Skills
AWS Data Engineering Services (EMR/Glue,Redshift,Aurora, S3, Lambda), Spark, Python, Collibra, Snowflake/Databricks, Tableau.
Roles & Responsibilities
- Ingest and model data from APIs, files/SFTP, and relational sources; implement layered architectures (raw/clean/serving) using PySpark/SQL and dbt, Python.
- Design and operate pipelines with Prefect (or Airflow), including scheduling, retries, parameterization, SLAs, and well documented runbooks.
- Build on cloud data platforms, leveraging S3/ADLS/GCS for storage and a Spark platform (e.g., Databricks or equivalent) for compute; manage jobs, secrets, and access.
- Publish governed data services and manage their lifecycle with Azure API Management (APIM) authentication/authorization, policies, versioning, quotas, and monitoring.
- Enforce data quality and governance through data contracts, validations/tests, lineage, observability, and proactive alerting.
- Optimize performance and cost via partitioning, clustering, query tuning, job sizing, and workload management.
- Uphold security and compliance (e.g., PII handling, encryption, masking) in line with firm standards.
- Collaborate with stakeholders (analytics, AI engineering, and business teams) to translate requirements into reliable, production ready datasets.
- Enable AI/LLM use cases by packaging datasets and metadata for downstream consumption, integrating via Model Context Protocol (MCP) where appropriate.
- Continuously improve platform reliability and developer productivity by automating routine tasks, reducing technical debt, and maintaining clear documentation.
- 4 15 years of professional data engineering experience.
- Strong Python, SQL, and Spark (PySpark) skills, and/or Kafka.
- Snowflake (Snowpipe, Tasks, Streams) as a complementary warehouse.
- Databricks (Delta formats, workflows, cataloging) or equivalent Spark platforms.
- Hands-on experience building ETL/ELT with Prefect (or Airflow), dbt, Spark, and/or Kafka.
- Experience onboarding datasets to cloud data platforms (storage, compute, security, governance).
- Familiarity with Azure/AWS/GCP data services (e.g., S3/ADLS/GCS; Redshift/BigQuery; Glue/ADF).
- Git-based workflows CI/CD and containerization with Docker (Kubernetes a plus).
Generic Managerial Skills, If any
- Strategic Technical Leadership: Defining data architecture, evaluating new technologies, and setting technical standards for AWS-based pipelines
- Stakeholder Communication: Bridging the gap between technical teams and business stakeholders, gathering requirements, and reporting progress
- Risk Management: Proactively identifying potential bottlenecks in data workflows, security risks, or scalability issues
- Operational Excellence: Implementing automation, optimizing costs, and maintaining high data quality standards.
