Associate Director, Data Platforms — Technical Lead

About the Role:

Grade Level (for internal use):

12 Key Responsibilities

Data Pipeline Transition and Platform Delivery

Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target data platform.

Drive the implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets.

Ensure pipelines are designed and managed in a way that supports long-term platform consistency, reliability, observability, and ease of support.

Guide the design and operation of cloud-native data pipelines using AWS services such as:

Amazon S3 for durable data lake storage

AWS Glue for integration, cataloging, and processing

AWS Lambda for event-driven processing

Amazon Kinesis for streaming use cases

AWS Lake Formation for governed data lake controls

Promote the use of AWS IAM, encryption, environment-level controls, and platform guardrails to enforce secure access to platform resources and data products.

Support practical application of lakehouse technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, and governed data access patterns.

Data Onboarding and Asset-Agnostic Enablement

Define and operationalize onboarding patterns that support a broad range of data assets, domains, and source systems without requiring bespoke platform redesign for each use case.

Work with platform, data engineering, architecture, governance, and business-aligned teams to simplify and standardize how data is ingested, transformed, governed, and published to the enterprise platform.

Create or contribute to reusable technical assets such as design patterns, reference implementations, onboarding templates, pipeline frameworks, technical documentation, and operational runbooks.

Support asset-agnostic onboarding by ensuring data pipelines are configurable, metadata-driven, scalable, and aligned with enterprise data platform standards.

Data Mastering Platform Integration

Support integration of platform pipelines and datasets with the enterprise data mastering platform.

Collaborate with upstream and downstream stakeholders to ensure mastered data can be consumed reliably through standardized interfaces and governed data flows.

Help establish data quality controls, reconciliation processes, metadata alignment, and stewardship workflows required to support trusted mastered data in the platform.

Contribute to issue resolution and continuous improvement related to mastering-related ingestion and distribution workflows.

Support data mastering capabilities aligned with platforms such as NeoXam DataHub, including: Data acquisition, Cleansing, Enrichment, Mastering, Reconciliation, Golden copy generation & Downstream distribution of trusted data products.

Technical Leadership and Engineering Excellence

Serve as a senior technical individual contributor for data platform engineering, providing expertise across pipeline migration, lakehouse architecture, AWS-native data engineering, governance, and mastering integrations.

Influence technical direction without direct people management responsibility.

Contribute to architecture discussions, design reviews, implementation planning, code reviews, technical standards, and production readiness reviews.

Translate broader architectural direction into actionable engineering patterns, implementation plans, and technical deliverables.

Promote engineering best practices including: Version control, Automated testing, CI/CD, Release automation, Monitoring and alerting, Incident response, Documentation.

Help establish cloud engineering standards for infrastructure automation, release management, and environment promotion using tools and services such as AWS CodePipeline, AWS CodeBuild, and infrastructure automation frameworks.

Drive operational rigor across production data pipelines, including observability, logging, telemetry, support models, service ownership, and incident management.

Collaboration, Governance, and Platform Standards

Partner effectively with global platform, architecture, governance, security, Databricks-aligned, and data mastering teams to ensure delivery aligns with enterprise standards.

Act as a technical bridge between platform strategy and engineering execution.

Support governance requirements through appropriate controls around:

Data lineage

Schema consistency

Data quality

Metadata

Retention

Access control

Encryption

Auditability

Secure data distribution

Ensure monitoring and operational health practices are in place using logging, alerting, telemetry, dashboards, and AWS-native operational tooling where appropriate.

Required Qualifications

8+ years of experience in data engineering, data platforms, cloud data architecture, or related engineering domains.

Proven ability to drive technical initiatives and influence engineering outcomes in a complex, execution-focused environment.

Experience partnering with global or distributed teams to deliver platform and pipeline initiatives across time zones.

Technical Expertise

Strong hands-on experience with modern data engineering and pipeline development, including batch and/or streaming data workflows.

Experience working with Databricks-based data platforms and supporting migration or transition of pipelines into a lakehouse-oriented architecture.

Strong familiarity with AWS cloud-native data engineering, including services such as Amazon S3, AWS Glue, AWS Lambda, AWS Lake Formation, Amazon Kinesis, AWS IAM, and AWS-native monitoring, logging, and security capabilities.

Working knowledge of technologies and concepts such as Delta Lake, Apache Iceberg, Databricks Unity Catalog, metadata-driven pipelines, data lake governance, lakehouse architecture, and catalog-driven processing.

Experience supporting data integration patterns involving mastering, MDM, reference data, market data, investment data, risk data, or trusted data distribution workflows.

Solid understanding of data quality, schema management, lineage, metadata, access control, encryption, and operational support for production data pipelines.

Experience with AWS IAM, data lake governance, policy-based access controls, and secure platform operations.

Familiarity with engineering best practices such as version control, automated testing, CI/CD, infrastructure automation, release management, and monitoring.

Experience designing or implementing data platform observability and reliability practices, including alerting, monitoring, telemetry, operational dashboards, and production support procedures.

AI Tooling and LLM-Enabled Engineering

Practical experience using AI tooling such as Claude, large language models, or similar AI-assisted development platforms in an engineering context.

Experience applying LLMs to data engineering or software engineering workflows, including code generation, refactoring, test creation, documentation, debugging, troubleshooting, and pipeline analysis.

Ability to use AI-assisted workflows to support Databricks, AWS data engineering, SQL, PySpark, pipeline migration, data quality analysis, and operational support activities.

Ability to evaluate, validate, and refine AI-generated code or recommendations before applying them to production-grade engineering work.

Collaboration and Execution

Strong communication and stakeholder management skills, with the ability to work effectively across US-based, offshore, and global teams.

Ability to translate broader architectural direction into actionable technical deliverables, implementation plans, and reusable engineering patterns.

Comfortable operating in a role that blends technical strategy, architectural influence, hands-on engineering, and delivery enablement.

Grade: 12 {9 to 13 years of experience}

Location: Hydera