Data Lake – Consultant (Cyber Defence & Resilience) at Deloitte

Apply for the Data Lake Consultant role in Deloitte Cyber (Chennai/Bangalore/Hyderabad/Pune). Engineer cloud data lakes, build ML models for anomaly detection, and design cybersecurity use cases using Python, Spark, and SQL.

About the Company: Deloitte

Deloitte is a premier global provider of audit, consulting, financial advisory, risk advisory, tax, and related services. Driven by a central purpose to make an impact that matters, Deloitte fosters an inclusive corporate culture that empowers its professionals to solve the world’s most complex business and technology challenges through diverse thinking and deep technical innovation.

This specialised consulting position is housed within the elite Cyber Defence & Resilience practice under Deloitte Risk Advisory. The team assists Fortune 500 enterprises in defending against advanced threats by transforming modern Security Operations Centers (SOCs), building data analytics engines, and enhancing operational resilience. To anchor this expanding technical data domain, Deloitte is scale-recruiting engineering talent across its key Indian delivery centers in Chennai, Tamil Nadu; Bangalore, Karnataka; Hyderabad, Telangana; and Pune, Maharashtra.

About the Role: Data Lake – Consultant

Are you a cybersecurity data scientist or data engineer who excels at building massive data lakes and applying machine learning to hunt for hidden network anomalies? Deloitte Cyber is seeking a Data Lake Consultant to step into a technical delivery role on the Cyber Defence & Resilience team. This position is tailored for data professionals with 3 to 6 years of experience developing data processing pipelines and predictive models to uncover cyber threats.

As a Consultant, you will sit at the crossroads of advanced data engineering and offensive/defensive security operations. You will fully immerse yourself in designing data lake ingestion workflows, building automated extract-transform-load (ETL) structures, and developing machine learning pipelines (supervised, unsupervised, and semi-supervised) dedicated entirely to anomaly detection. Working closely with incident response squads, you will translate raw, high-velocity log data into highly visual, predictive cyber threat intelligence.

Key Responsibilities & Engineering Workflows

• Cyber Use Case Architecture: Support the structural design, hypothesis framing, and technical documentation of machine learning and statistics-based cybersecurity use cases.

• Data Lake ETL & Feature Engineering: Build, clean, and optimise large-scale security datasets for analytical modelling through robust data processing pipelines, feature engineering, and SQL-based transformations.

• Advanced ML Modelling: Develop, evaluate, and deploy machine learning models, autonomous workflows, and behavioural anomaly detection engines using Python and Apache Spark.

• Cloud Security Integration: Architect and deploy cloud-native analytical data solutions across multi-cloud infrastructure environments, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

• Threat Visualisation: Create interactive, high-impact analytical visualisations and executive-level threat dashboards using enterprise tools like Microsoft Power BI or Tableau.

• Incident Response Support: Partner directly with Security Operations Centre (SOC) teams to refine threat detection alerts, analyse raw event logs, and build frameworks that accelerate crisis and cyber incident response.

Candidate Prerequisites & Technical Skill Stack

Candidates must offer a balanced background in big data manipulation, mathematical modelling, and a foundational understanding of enterprise security architectures.

Required Experience & Educational Baseline:

• Educational Credentials: Bachelor’s degree in Analytics, Statistics, Mathematics, Computer Science, or another quantitative engineering field (or equivalent practical experience in a quantitative technical role).

• Professional Tenure: 3 to 6 years of experience writing code and training machine learning models explicitly focused on anomaly detection.

• Core Modelling Toolkit: Practical experience designing, training, and deploying experiments using supervised, unsupervised, and semi-supervised machine learning techniques.

• Data Stack Proficiencies: Advanced, fluid technical mastery of Python, Apache Spark, and SQL for data processing, large-scale modeling, and analytical workflows.

• Cloud Fluency: Hands-on experience working inside cloud ecosystems (AWS, Azure, or GCP) to manage data pipelines or host analytical models.

• Visualisation Skills: Proven experience developing intuitive, data-driven visualisation templates using Power BI or Tableau.

• Domain Context: Prior professional exposure to cybersecurity concepts, network security operations, IT risk management, or security log analysis.

Preferred Qualifications & Strategic Pluses:

• Delivery Frameworks: Experience operating within a client-facing consultative environment, product delivery house, or Agile software development squad.

• Collaborative Delivery: Background supporting distributed, cross-functional, or remote technical team delivery models.

• Security Framework Knowledge: Conceptual familiarity with standard cybersecurity use case frameworks (such as MITRE ATT&CK or cyber kill chain structures).

Core Position Specifications

• Position Title: Data Lake – Consultant

• Hiring Practice: Cyber Defence & Resilience (Deloitte Cyber)

• Corporate Requisition Code: 358459

• Available Job Locations: Chennai, Tamil Nadu (Primary) | Bangalore, Karnataka | Hyderabad, Telangana | Pune, Maharashtra

• Shift Pattern: General Shift Timings

• Experience Tier: 3 to 6 Years of professional quantitative/cyber experience

• Employment Framework: Full-Time, Permanent Consulting Track

• Core Technology Anchors: Python, Spark, SQL, Cloud Architecture (AWS/Azure/GCP), and Machine Learning Anomaly Detection