Senior Technical Manager

In this role, you will lead and manage our technical teams with a focus on data engineering, AI/ML, and delivering our flagship product- DATASteroid. This role is critical in driving team efficiency, aligning technical capabilities with the sales pipeline, building Databricks certifications and practice, and reducing bench strength. The Senior Technical Manager will work with a highly capable technical team in a hyper paced setup and work to meet client delivery and TechVariable’s business goals. This role involves close collaboration with the Founders and senior leadership to work in cohesion with business objectives- one who can operate with integrity, trust and aspirationally.

Roles and Responsibilities

1.  Technical Leadership: Define technical strategies and architectural direction for product development and provide hands-on guidance for complex technical challenges.
2.  Team Management: Lead and mentor software developers, engineers, and architects to foster growth and performance and also oversee resource allocation, task prioritization, and delivery schedules.
3.  Product Development Oversight:

• a. Collaborate with Product Managers to translate business needs into technical specifications
• b. Ensure timely and quality delivery of software products adhering to defined standards.
• c. Identify technical risks and devise mitigation strategies to avoid project delays or failures.
• d. Build best practices into internal and client-facing projects.

1.  Code Quality and Standards: Ensure adherence to coding, testing, and documentation standards across the teams. Promote best practices, code reviews, and continuous learning within the teams.
2.  Cross-functional Collaboration: Work closely with stakeholders, including UX/UI teams, QA, and DevOps, to streamline product workflows and maintain alignment between business objectives and technical deliverables.
3.  Performance Management of teams: Track and report team performance metrics, skill mapping and alignment, providing insights to leadership on progress and areas for improvement.
4.  Continuous Improvement: Foster a culture of innovation, continuous learning, and process improvement within the technical team.
5.  Technology Evaluation & Implementation:

• a. Assess emerging technologies to determine their potential impact on products and processes.
• b. Drive the adoption of tools and technologies to improve efficiency through training and upskilling.
• c. Align the Sales Pipeline with technical team hiring and upskilling.

Required Skills

● Education: BCA/MCA, BE/BTech in Computer Science,

● Technical Skills: Python, Snowflake, Pyspark, Kubernetes, Cloud technologies, Apache Kafka, Apache Spark, Pandas, Polars, Git, Docker.

● Experience: 10+ years of experience in software development, including 5+ years in a managerial role.

● Soft Skills: Great communication and team leadership skills

● Location – Guwahati

● Knowledge: Strong technical background with expertise in:

○ Core Data Engineering Skills (Data Architecture and Modelling, Big Data Technologies, Cloud Platforms)

○ Key technologies: Snowflake or Databricks

○ Programming & Scripting: (Languages: Python, SL, Scala or Java & Scripting: Bash, PowerShell, or other scripting languages for automation and deployment)

○ DevOps & CI/CD in Data Engineering (Familiarity with CI/CD tools like Jenkins, GitLab CI/CD, or Azure DevOps, Containerization tools like Docker and orchestration platforms like Kubernetes, Monitoring and logging tools (e.g., Prometheus, Grafana, Datadog)

○ Data Quality: data profiling techniques, building and integrating data quality frameworks into ETL/ELT pipelines, tools like Great Expectations, Apache Griffin, or DQ frameworks, expertise in defining and implementing data quality metrics, Knowledge of anomaly detection techniques and leveraging AI/ML for predictive data quality assurance, collaboration with business stakeholders to define data quality rules and maintain comprehensive documentation.

○ Data Governance and Security: Understanding of data privacy regulations (e.g., GDPR, CCPA)., implementing data governance tools and frameworks like Collibra or Alation, setting up RBAC (Role-Based Access Control), encryption, and auditing for data systems.