Ventures Unlimited Inc · 17 hours ago
Data Engineering Lead
New York, NY
Full-time
Onsite
Senior Level, Lead/Staff
4+ years expVentures Unlimited Inc is seeking a Data Engineering Lead to oversee data ingestion, modeling, and pipeline design using AWS services and other technologies. The role involves collaborating with stakeholders to ensure data quality and governance while optimizing performance and cost in data workflows.
ConsultingInformation TechnologyStaffing Agency
Hiring Manager
Prabhakar Choudhary
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
Qualification
Required
AWS Data Engineering Services (EMR/Glue, Redshift, Aurora, S3, Lambda)
Spark
Python
Collibra
Snowflake/Databricks
Tableau
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)
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
Company
Ventures Unlimited Inc
We are a cutting-edge consulting firm specializing in Technology Solutions.
51-200 employees
Funding
Current Stage
Growth StageCompany data provided by crunchbase