Data Engineer Manager

About the role

Job Description: Develops or modifies program logic for new applications or software which may include coding, testing, debugging, documenting, implementing and maintaining software applications. Analyzes requirements, and maintains, tests and integrates application components. Programmer Analyst roles should be assigned to this job family. Accountabilities Design and implement scalable data pipelines for structured and unstructured data. Develop and maintain data models, integration frameworks, and storage solutions (cloud/on-prem/hybrid). Create scalable and reliable ETL/ELT processes to extract, transform, and load data from various sources into data warehouses or data lakes. Ensure adherence to data governance, security, and compliance standards (GDPR, HIPAA, SOX). Implement best practices for data quality, lineage, and metadata management. Define and maintain the overall data solution architecture, including integration patterns, data models, and governance frameworks. Collaborate with business stakeholders to translate requirements into technical designs that meet performance, security, and compliance standards. Oversee implementation of data platforms (e.g., cloud, on-premise, hybrid) ensuring scalability and interoperability. Establish and enforce architecture principles, standards, and best practices across development teams. Provide technical leadership during project lifecycle: design reviews, solution validation, and deployment strategies. Partner with IT security and compliance teams to embed security controls and regulatory adherence into all solutions. Evaluate emerging technologies and recommend adoption strategies aligned with enterprise goals. Clean, transform, and organize raw data from multiple sources into usable formats. Implement and enforce data quality, security, and governance policies, and ensure data is backed up and accessible. Work with data scientists, analysts, and stakeholders to understand business needs and provide data for analytics and decision-making. Evaluate and optimize existing data systems for improved performance, reliability, and speed. Create analytical tools and programs to help with data analysis and reporting. Partner with IT, BI, and business teams to translate requirements into technical solutions. Support advanced analytics initiatives by enabling reliable and accessible data infrastructure. Job Requirements: Knowledge, Skills, and Experience Bachelor's or Master's degree in Computer Science, Information Systems, or related field. 5+ years of experience in data engineering. Proven experience with ETL tools, cloud platforms (such as Azure, AWS, GCP), and big data technologies. Strong understanding of enterprise data architecture, data modeling, and integration frameworks. Knowledge of cloud platforms (e.g., Azure, AWS, GCP) and data services (e.g., Snowflake, Databricks). Familiarity with compliance standards (GDPR, HIPAA, SOX) and security best practices. Critical Skills / Technical Know-How Expertise in designing scalable data solutions using modern architectures (microservices, event-driven, API-first). Proficiency in ETL/ELT tools, data warehousing, and big data technologies. Proficiency in programming languages such as SQL and Python, experience with database systems, and familiarity with big data technologies. Ability to lead architecture reviews and communicate complex technical concepts to non-technical stakeholders. Strong problem-solving and analytical skills with experience in performance tuning and optimization. Ability to communicate requirements and results with both technical and non-technical team members. Competencies Strategic Thinking - Aligns architecture decisions with long-term business objectives. Technical Expertise - Demonstrates mastery of data architecture and emerging technologies. Collaboration & Influence - Builds strong relationships across IT and business teams. Decision Making - Makes informed, timely decisions under uncertainty. Innovation - Identifies and applies innovative solutions to complex problems. Change Agility - Adapts quickly to evolving technologies and business needs. Critical Success Factors & Key Challenges Delivering robust, secure, and scalable data solutions that meet business needs. Driving standardization and governance across diverse systems and teams. Maintaining strong stakeholder engagement and clear communication throughout projects. Key Challenges Balancing innovation with compliance and security requirements. Managing complexity in multi-cloud and hybrid environments. Ensuring interoperability and data quality across global systems. #LI-CY1 IND123 Target Market Salary Range: Actual compensation packages take into account a wide range of factors that are unique to each candidate, including but not limited to geographic location; skill sets; relevant education and certifications; depth of experience; performance; and other business and organizational needs. The disclosed reasonable estimate