Lead Developer

Job Summary

Act as a senior technical authority and lead developer for data engineering initiatives, delivering scalable data solutions that leverage SQL, Azure Databricks, Azure Data Lake Store, and Azure Data Factory within a hybrid work model. Support the creation of high-quality data platforms for investment banking–oriented analytics and operational reporting, working in a collaborative day shift environment.

Responsibilities

Design and deliver reliable data engineering solutions using SQL, Azure Databricks, Azure Data Lake Store, and Azure Data Factory to provide secure, scalable data pipelines for complex analytics requirements within investment banking settings

Build efficient batch and near real time data pipelines that ingest, transform, and curate structured and semi structured data to support high quality downstream reporting and regulatory analysis

Develop and optimize SQL queries, stored procedures, and data models to improve query performance, reduce compute costs, and ensure consistent and dependable data access for analytical users and operational teams

Set up and maintain Azure Databricks clusters, notebooks, and jobs to orchestrate data transformations, manage resource utilization, and promote consistent engineering practices

Create and manage Azure Data Factory pipelines, triggers, and activities to automate data movement between multiple sources and sinks, enforcing standardized approaches and reusable components

Administer Azure Data Lake Store storage structures, access patterns, and data lifecycle policies to ensure organized storage, cost effectiveness, and secure handling of sensitive financial information

Partner with business analysts and stakeholders from investment banking operations to convert requirements into clear technical specifications and data transformations aligned with business goals

Work with quality engineering and operations teams to implement validation checks, reconciliation controls, and monitoring processes that confirm data completeness, accuracy, and timeliness

Mentor peers on coding best practices, testing, documentation, and version control to uphold high engineering standards and consistent delivery of maintainable data solutions

Conduct root cause analysis for data defects, production incidents, and performance bottlenecks, then apply long lasting fixes and preventive improvements to strengthen platform stability

Produce clear documentation for solution designs, data flow diagrams, data dictionaries, and operational runbooks so cross functional teams can easily understand, use, maintain, and extend the platforms

Coordinate with architecture and security teams to ensure implementations follow enterprise reference patterns, meet compliance expectations, and support resiliency goals across the Azure data platform

Participate in sprint planning, backlog refinement, and technical reviews to ensure stories are realistically scoped, risks are identified early, and stakeholder deliverables meet quality expectations

Qualifications

Bring extensive hands on experience with SQL, Azure Databricks, Azure Data Lake Store, and Azure Data Factory, with the ability to design end to end data pipelines that support analytics for financial services

Use knowledge of investment banking operations or investment banking and brokerage processes when available to shape data models, transformation rules, and controls that reflect real world trade and post trade workflows

Show strong understanding of data warehousing concepts, dimensional modeling, and performance tuning methods that enable high volume and low latency analytical workloads

Leverage experience working in hybrid environments and collaborating with cross functional teams to maintain clear communication, effective documentation, and timely delivery across distributed teams

Demonstrate strong problem solving skills, attention to detail, and a continuous improvement mindset that support dependable production operations and ongoing enhancements to the data platform