Gotham Technology Group
Sr. Technical Recruiter at Gotham Technology Group
Title: Enterprise Data Management – Data Cloud, Senior Developer I
Location: Hybrid, 2 days onsite in NYC
Duration: FTE/Permanent
The Data Engineering team oversees the organization’s central data infrastructure, which powers enterprise-wide data products and advanced analytics capabilities in the investment management sector. We are seeking a senior cloud data engineer to spearhead the architecture, development, and rollout of scalable, reusable data pipelines and products, emphasizing the creation of semantic data layers to support business users and AI‑enhanced analytics. The ideal candidate will work hand‑in‑hand with business and technical groups to convert intricate data needs into efficient, cloud‑native solutions using cutting‑edge data engineering techniques and automation tools.
Responsibilities
Collaborate with business and technical stakeholders to collect requirements, pinpoint data challenges, and develop reliable data pipeline and product architectures.
Design, build, and manage scalable data pipelines and semantic layers using platforms like Snowflake, dbt, and similar cloud tools, prioritizing modularity for broad analytics and AI applications.
Create semantic layers that facilitate self‑service analytics, sophisticated reporting, and integration with AI‑based data analysis tools.
Build and refine ETL/ELT processes with contemporary data technologies (e.g., dbt, Python, Snowflake) to achieve top‑tier reliability, scalability, and efficiency.
Incorporate and automate AI analytics features atop semantic layers and data products to enable novel insights and process automation.
Refine data models (including relational, dimensional, and semantic types) to bolster complex analytics and AI applications.
Advance the data platform’s architecture, incorporating data mesh concepts and automated centralized data access.
Champion data engineering standards, best practices, and governance across the enterprise.
Establish CI/CD workflows and protocols for data assets to enable seamless deployment, monitoring, and versioning.
Partner across Data Governance, Platform Engineering, and AI groups to produce transformative data solutions.
Qualifications
Bachelor’s or Master’s in Computer Science, Information Systems, Engineering, or equivalent.
10+ years in data engineering, cloud platform development, or analytics engineering.
Extensive hands‑on work designing and tuning data pipelines, semantic layers, and cloud‑native data solutions, ideally with tools like Snowflake, dbt, or comparable technologies.
Expert‑level SQL and Python skills,
plus deep familiarity with data tools such as Spark, Airflow, and cloud services (e.g.,
Snowflake , major
hyperscalers ).
Preferred: Experience containerizing data workloads with Docker and Kubernetes.
Track record
architecting semantic layers, ETL/ELT flows, and cloud integrations for AI/analytics scenarios.
Knowledge of semantic modeling, data structures (relational/dimensional/semantic), and enabling AI via data products.
Bonus: Background in data mesh designs and automated data access systems.
Skilled in dev tools like Azure DevOps equivalents, Git‑based version control, and orchestration platforms like Airflow.
Strong organizational skills, precision, and adaptability in fast‑paced settings with tight deadlines.
Proven self‑starter who thrives independently and collaboratively, with a commitment to ongoing tech upskilling.
Bonus: Exposure to BI tools (e.g., Tableau, Power BI), though not central to the role.
Familiarity with investment operations systems (e.g., order management or portfolio accounting platforms).
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Information Technology
Industries IT Services and IT Consulting and Financial Services
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Location: Hybrid, 2 days onsite in NYC
Duration: FTE/Permanent
The Data Engineering team oversees the organization’s central data infrastructure, which powers enterprise-wide data products and advanced analytics capabilities in the investment management sector. We are seeking a senior cloud data engineer to spearhead the architecture, development, and rollout of scalable, reusable data pipelines and products, emphasizing the creation of semantic data layers to support business users and AI‑enhanced analytics. The ideal candidate will work hand‑in‑hand with business and technical groups to convert intricate data needs into efficient, cloud‑native solutions using cutting‑edge data engineering techniques and automation tools.
Responsibilities
Collaborate with business and technical stakeholders to collect requirements, pinpoint data challenges, and develop reliable data pipeline and product architectures.
Design, build, and manage scalable data pipelines and semantic layers using platforms like Snowflake, dbt, and similar cloud tools, prioritizing modularity for broad analytics and AI applications.
Create semantic layers that facilitate self‑service analytics, sophisticated reporting, and integration with AI‑based data analysis tools.
Build and refine ETL/ELT processes with contemporary data technologies (e.g., dbt, Python, Snowflake) to achieve top‑tier reliability, scalability, and efficiency.
Incorporate and automate AI analytics features atop semantic layers and data products to enable novel insights and process automation.
Refine data models (including relational, dimensional, and semantic types) to bolster complex analytics and AI applications.
Advance the data platform’s architecture, incorporating data mesh concepts and automated centralized data access.
Champion data engineering standards, best practices, and governance across the enterprise.
Establish CI/CD workflows and protocols for data assets to enable seamless deployment, monitoring, and versioning.
Partner across Data Governance, Platform Engineering, and AI groups to produce transformative data solutions.
Qualifications
Bachelor’s or Master’s in Computer Science, Information Systems, Engineering, or equivalent.
10+ years in data engineering, cloud platform development, or analytics engineering.
Extensive hands‑on work designing and tuning data pipelines, semantic layers, and cloud‑native data solutions, ideally with tools like Snowflake, dbt, or comparable technologies.
Expert‑level SQL and Python skills,
plus deep familiarity with data tools such as Spark, Airflow, and cloud services (e.g.,
Snowflake , major
hyperscalers ).
Preferred: Experience containerizing data workloads with Docker and Kubernetes.
Track record
architecting semantic layers, ETL/ELT flows, and cloud integrations for AI/analytics scenarios.
Knowledge of semantic modeling, data structures (relational/dimensional/semantic), and enabling AI via data products.
Bonus: Background in data mesh designs and automated data access systems.
Skilled in dev tools like Azure DevOps equivalents, Git‑based version control, and orchestration platforms like Airflow.
Strong organizational skills, precision, and adaptability in fast‑paced settings with tight deadlines.
Proven self‑starter who thrives independently and collaboratively, with a commitment to ongoing tech upskilling.
Bonus: Exposure to BI tools (e.g., Tableau, Power BI), though not central to the role.
Familiarity with investment operations systems (e.g., order management or portfolio accounting platforms).
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Information Technology
Industries IT Services and IT Consulting and Financial Services
#J-18808-Ljbffr