Data Engineering Manager

Overview Join to apply for the Data Engineering Manager role at FinStrat Management . The Role: We are looking for a highly skilled Data Engineer with strong expertise in Python programming, data processing, and analytical problem-solving. This role requires a blend of analytical skills, engineering capabilities, and hands-on data manipulation to derive actionable insights, build efficient pipelines, and support data-driven decision-making across teams. Responsibilities Leadership: Lead, mentor, and grow a team of data engineers responsible for building and maintaining our data infrastructure; define the data engineering roadmap and align priorities with finance and business objectives; present project status, data insights, and risk assessments to executive leadership and non-technical audiences. Data Exploration & Analysis: Analyze large and complex datasets to extract meaningful insights and drive decision-making processes; identify data trends, anomalies, and opportunities for improvement; collaborate with cross-functional teams to translate business requirements into technical solutions. Data Pipeline Development: Design, develop, and maintain robust data pipelines for ingestion, transformation, and storage; optimize and automate data workflows to improve data availability, quality, and processing efficiency; implement ETL processes to support analytics and reporting needs. Data Modeling & Feature Engineering: Build, validate, and maintain data models to support machine learning and statistical analysis; engineer and preprocess features for ML algorithms; develop scalable solutions for feature storage, retrieval, and real-time model serving. Programming & Scripting: Write efficient, scalable, and well-documented Python code; collaborate on code reviews, optimize performance, and apply best practices in coding and version control; use Python libraries (e.g., Pandas, NumPy, SQLAlchemy) to streamline data workflows. Performance Optimization & Troubleshooting: Monitor, troubleshoot, and enhance the performance of data systems and pipelines; address data integrity and pipeline issues to ensure reliable data availability and uptime; implement monitoring and logging to preemptively detect issues. Collaboration & Communication: Work with data scientists, analysts, and other engineers to develop cohesive data solutions; translate complex technical issues into non-technical language for stakeholders; contribute to documentation, data standards, and best practices to foster a data-centric culture. Job Requirements Technical Skills: Strong proficiency in Python and familiarity with data processing libraries (e.g., Pandas, NumPy, PySpark); experience with SQL for data extraction and manipulation. Data Engineering Knowledge: Experience designing, building, and managing data pipelines, ETL workflows, and data warehousing solutions. Statistical & Analytical Skills: Ability to apply statistical methods for data analysis and familiarity with machine learning concepts. Problem-Solving Mindset: Proven ability to troubleshoot complex data issues and continuously improve workflows for efficiency and accuracy. Communication: Effective communication skills to convey data insights to technical and non-technical stakeholders alike. Bonus: Experience with cloud platforms (e.g., AWS, GCP), containerization (e.g., Docker), and orchestration tools (e.g., Airflow) is a plus. Preferred Education & Experience Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field. 3+ years of experience in a data science or data engineering role. Benefits Compensation commensurate with experience Unlimited vacation Ongoing education and training Bonuses and profit-sharing Seniority level Mid-Senior level Employment type Full-time Job function Engineering and Information Technology Industries We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI. #J-18808-Ljbffr