Senior BigID Developer & Operations Specialist (with NLP Expertise)

About the Role

We are looking for an experienced Power BI Architect to lead enterprise-wide BI initiatives. This role will be responsible for designing, implementing, and governing scalable Power BI solutions across the organization. The ideal candidate will play a key role in establishing best practices, driving adoption, and owning the overall BI architecture within the Center of Excellence (CoE).

Key Responsibilities

• Lead end-to-end implementation of Power BI solutions across the enterprise
• Design scalable, secure, and high-performance BI architecture
• Act as the Power BI Architect, providing technical leadership and direction
• Own and drive BI governance, standards, and best practices within the CoE
• Collaborate with business and technology teams to translate requirements into robust BI solutions
• Define data modeling strategies, reporting frameworks, and dashboard standards
• Ensure data accuracy, consistency, and performance optimization across reports
• Support and guide development teams in Power BI implementation
• Enable enterprise-wide adoption of Power BI through training, documentation, and mentorship
• Work closely with IT/technology teams to integrate Power BI with data platforms and enterprise systems

Required Skills & Qualifications

• 10+ years of experience in BI/Data Analytics, with strong focus on Power BI
• Proven experience in enterprise-wide Power BI implementation
• Strong expertise in:
• Power BI (Desktop, Service, Gateway, Dataflows)
• Data Modeling (Star Schema, Snowflake Schema)
• DAX, Power Query (M language)
• Experience in designing and implementing BI architecture at scale
• Strong understanding of data warehousing concepts and ETL processes
• Experience working within a Center of Excellence (CoE) model
• Excellent stakeholder management and communication skills

Preferred Skills

• Experience with Azure (Azure Synapse, Data Factory, Data Lake)
• Knowledge of DevOps practices for Power BI (CI/CD pipelines)
• Exposure to governance tools and security frameworks
• Certification in Power BI / Microsoft Data Platform

The role

A working product and a working deployment are two different things. You are the person who closes that gap.

You sit inside the client's head office. You get the deployment live, you get their teams using the dashboards, and you own whether the AI is returning something worth acting on. Every client is different: different languages on the floor, different store noise, different vocabulary for the same product, different CRM, different idea of what a good conversation looks like. The core platform does not change for each of them. You are the layer that makes it fit, and you are the one the client meets.

You are encouraged to spend time in stores. The engineers who do the best work here are the ones who have stood on a shop floor and watched where the pitch and the pipeline actually break. Nobody will make you go. You will also spend real time in the codebase, because you fix what you find rather than filing it.

What you build has a commercial edge to it. A pilot converts when the client sees the result they were promised, and an account grows when a second team inside it sees what is already sitting in their data. Both of those outcomes are yours to deliver, not somebody else's to chase.

How you will work

You design the deployment, you build it, and you own whether it holds up on a Saturday evening in a crowded store. Nobody hands you the plan, and nobody hands you the spec. You write both.

The decisions are yours. Which integration is worth the week, which vertical taxonomy needs building, what ships in the pilot and what waits, and when to tell a client that the thing they are asking for is the wrong thing to build. You go and find out what a client needs before anyone writes a line of code.

You will not be doing it alone. There are founders, AI engineers and product people around you, and they will build alongside you. What nobody will do is tell you what the client needs. That call is yours.

The work compounds if you do it well. What you learn on one deployment becomes a specification, then code, then a pattern the next one starts from. A year in, the deployments you designed should be running without you, and a new client should take a fraction of the time the first one did.

What you will do

Own the deployment end to end. Device provisioning, store connectivity, data flowing, first insight in front of the client. Get from kickoff to something real inside

the pilot window, and know by the halfway mark whether it is in trouble.

Get their HQ using it. A dashboard nobody opens is a failed deployment. Sit with the sales, marketing and L&D teams, show them what is in their own data, and

make sure the people who asked for this are actually looking at it every week.

Make the AI work on their floor. Their languages, their store noise, their product vocabulary. Benchmark transcription and speaker separation on their actual

audio, and fix what fails instead of explaining it away.

Build the vertical. Intent taxonomies, objection maps and prompt libraries for the category you are deployed into. A jewellery floor and an electronics floor do not

share a conversation model.

Wire it into their systems. CRM and POS integrations, so conversation data connects to what actually got sold and the insight can be checked against reality.

Build what the client asks for. Custom reports, dashboards and agents. Ground everything in source conversations and verify it before it ships, because a confident

wrong number costs an account.

Close the pilot. A pilot converts on results, not on effort. Know what the client agreed to judge this on, work backwards from it, and make sure the output in front of

their leadership at the end is the thing they asked for.

Grow the account. The same intelligence is worth something to marketing, L&D and category teams inside the same client. Spot which of them would benefit, show them what is already in their data, and hand a real opening to the account team.

Push it back into the product. Turn one-off client work into something the platform does by default, so the next deployment starts further ahead than this one did.

What we are looking for

Must have

• 0 to 5 years of experience. A consulting internship or an analyst role is the closest match to what this job actually asks for, but we care more about what you can do than where you did it
• Coding ability, ideally Python. Degree, internship, first job or your own projects. What we want to see is something you built that other people actually used
• Excel or Sheets at a real working level. A lot of the first conversation with a client happens in a spreadsheet before it ever happens in a dashboard
• The ability to explain a complicated idea simply. You will be taking AI output to people who do not think about models, and the explanation matters as much as the result
• Comfort at the boundaries. APIs, data pipelines, some frontend, some hardware when a device misbehaves
• An eye for where a deployment turns into more business, and the willingness to raise it yourself
• Heavy hands-on LLM usage. Prompts, evaluations, retrieval, and a clear view on where these tools break
• Fluent English and Hindi. A third Indian language counts for a lot, since the useful conversations happen on store floors and not only in HQ meeting rooms
• The instinct to go and find out what a client needs rather than waiting to be told

Good to have

Speech or audio work. Transcription, diarization, voice activity detection, or

anything that survives noisy real-world recording

Embedded or IoT experience, on ESP32 or similar

SQL and experience building things customers actually look at

Side projects, hackathons or internships where you shipped without a spec

and it worked

Technical Lead 

Job Description 

Role Overview 

We are seeking a highly skilled Data Engineering Lead with minimum of 5 years of hands-on experience in Azure data engineering, data warehousing, and automation-driven  integration. The ideal candidate should be a proven technical lead, capable of driving end-to-end 

project delivery and working closely with customer teams. This is a Work from Office / Customer Site role. 

Roles and Responsibilities 

• Lead the design, development, and delivery of data engineering and automation projects. 

• Architect and implement ETL/ELT pipelines using Azure Data Factory. 

• Design and manage enterprise data warehouses including dimensional modeling and 

schema optimization. 

• Manage Azure components including Storage Accounts, Data Lakes, Azure SQL, and Synapse. 

• Drive automation initiatives across data ingestion and transformation workflows. 

• Collaborate with business and technical stakeholders to translate requirements into scalable solutions. 

• Mentor team members and enforce engineering best practices. 

• Serve as the technical anchor responsible for ensuring high-quality, on-time delivery. 

Skills and Qualification 

• Strong hands-on experience with Azure Data Factory, Azure Storage, Azure SQL/Synapse. 

• Deep understanding of data warehousing concepts: star/snowflake schemas, fact/dimension modeling. 

• Experience with automation-led data engineering solutions. 

• Strong troubleshooting, optimization, and analytical skills. 

• Excellent communication and stakeholder management abilities. 

• Proven experience as a Technical Lead leading teams and delivery. 

Must Have 

• Min of 5 years of relevant data engineering experience. 

• Strong Azure Data Engineering and Data Warehousing expertise. 

• Proven Technical Lead experience. 

• Ability to work from office and customer site. 

• Strong ownership mindset with a focus on quality and delivery excellence.

Job Description: Lead - Cloud Engineering (AWS / Azure)

Role Title: Lead - Cloud Engineering

Experience Level: 10+ Years

Domain Focus: Healthcare AI & Cloud Infrastructure

Location: Remote

Job Overview

We are seeking an experienced Lead - Cloud Engineering with over 10 years of IT experience to lead our cloud strategy, architecture, and infrastructure teams. In this role, you will oversee end-to-end cloud deployment, multi-cloud migration, and scalable architecture designed to support cutting-edge Generative AI applications in the healthcare technology domain.

The ideal candidate brings deep technical expertise in both AWS and Azure, strong hands-on capability in cloud infrastructure, and proven leadership experience driving security, compliance, and team growth.

Key Responsibilities

Cloud Architecture & Migration

• Lead the architecture, design, and execution of cloud migrations, deployments, and modernizations across AWS and Azure environments.
• Drive Infrastructure as Code (IaC) standards using Terraform, CloudFormation, or Bicep to ensure scalable, automated infrastructure provisioning.
• Build high-availability, low-latency architectures optimized for data-intensive Generative AI and Machine Learning workloads.

Security & Healthcare Compliance

• Enforce healthcare security standards including HIPAA, HITRUST, SOC 2, and data governance best practices across all cloud assets.
• Implement Zero-Trust security, Identity Access Management (IAM), data encryption key management, and continuous vulnerability monitoring.

Leadership & Team Management

• Manage, mentor, and scale a high-performing team of DevOps, Cloud, and SRE Engineers.
• Drive Agile workflows, sprint planning, incident response frameworks, and SLA compliance.
• Collaborate closely with Data Engineering, AI/ML, and Software Product teams to align infrastructure with business roadmaps.

Operations & FinOps

• Establish cloud cost optimization strategies (FinOps) to manage computing costs associated with AI models and large-scale data processing.
• Manage monitoring, alerting, and telemetry frameworks (e.g., Prometheus, Datadog, CloudWatch) to ensure 99.99% uptime.

Key Requirements

• Experience: 10+ years of overall IT experience with at least 5+ years in a cloud leadership or lead architect role.
• Cloud Platforms: Advanced hands-on expertise with both AWS (e.g., EC2, S3, EKS, Bedrock, SageMaker) and Azure (e.g., AKS, Azure OpenAI, Blob, Virtual Machines).
• DevOps & IaC: Strong background in Terraform, Docker, Kubernetes, CI/CD pipelines (GitHub Actions, GitLab CI, or Jenkins).
• Domain Knowledge: Prior experience building or managing cloud environments within Healthcare, Life Sciences, or HealthTech is strongly preferred.
• AI/ML Familiarity: Experience supporting cloud infrastructure for machine learning pipelines, LLM deployments, or GPU compute management.
• Certifications (Preferred): AWS Certified Solutions Architect – Professional, Azure Solutions Architect Expert, or Certified Kubernetes Administrator (CKA).

We are looking for a Lead Data Architect to design, build, and scale our data pipelines and entity resolution systems. This role combines deep technical expertise in data engineering with hands-on experience in AI-assisted tooling, entity matching, and data integration from diverse sources. You will lead architectural decisions for our data platform, mentor engineers, and ensure our pipelines are reliable, scalable, and production-grade.

Key Responsibilities

     Architect, build, and maintain robust, scalable data pipelines that ingest, transform, and serve data from multiple internal and external sources.

     Own the end-to-end orchestration of data workflows using tools like Dagster, ensuring observability, reliability, and maintainability of pipelines.

     Design and implement entity resolution workflows — including matching, merging, and survivorship logic — using tools such as Splink, to produce clean, deduplicated, golden records.

     Build and maintain web scrapers to source data from external providers, ensuring resilience to source changes, rate limits, and data quality issues.

     Integrate and reconcile data coming from multiple, often inconsistent, sources into unified, trustworthy datasets.

     Design and maintain data models and schemas across transactional and analytical systems, ensuring consistency, scalability, and performance.

     Leverage AI/LLM-based tools and techniques to enhance data pipeline capabilities — e.g., intelligent data extraction, automated data quality checks, or AI-assisted entity matching.

     Define and enforce best practices around pipeline design, testing, monitoring, and documentation.

     Collaborate closely with data engineers, product managers, and other stakeholders to translate business requirements into scalable data architecture.

     Provide technical leadership and mentorship to the data engineering team.

Required Skills & Experience

     Strong hands-on experience building and maintaining production-grade data pipelines at scale.

     Practical experience with Dagster (or similar orchestration tools like Airflow/Prefect) for pipeline orchestration.

     Experience with Splink or similar probabilistic/deterministic record linkage tools for entity matching, merging, and survivorship.

     Strong proficiency in Python, including experience writing and maintaining web scrapers.

     Proven experience integrating and maintaining data pipelines that pull from multiple, heterogeneous data sources.

     Experience applying AI/ML tools within data engineering workflows (e.g., LLM-assisted data cleaning, extraction, or matching).

     Hands-on experience with relational and distributed databases such as PostgreSQL and Google Cloud Spanner.

     Strong understanding of data modeling principles (normalization, dimensional modeling, schema design) across OLTP and OLAP systems.

     Experience with cloud data warehousing platforms such as BigQuery, Redshift, and cloud platforms (GCP/AWS/Azure).

     Strong communication skills and experience working cross-functionally with engineering and product teams.

     Experience with distributed data processing frameworks (e.g., Spark, Dask).

     Familiarity with data governance, lineage, and cataloging tools.

     Prior experience in a lead or architect-level role guiding a data engineering team.

What We're Looking For

A technically strong, hands-on leader who can balance architectural thinking with the practical grit of debugging a flaky scraper or tuning a matching algorithm — someone who's comfortable owning both the big picture and the messy details of real-world data.

Next

Forward Deployed Implementation Engineer

About the Role

We are looking for a Forward Deployed Implementation Engineer who can work closely with customers and internal teams to successfully implement and operationalize our product.

This is a customer-facing, execution-oriented role that sits at the intersection of Product, Engineering, Accounting, and Customer Operations.

You will own implementations from start to finish — understanding customer requirements, configuring the product, setting up workflows and integrations, troubleshooting issues, coordinating with Engineering/Product, and ensuring customers are successfully onboarded and operational.

This role is ideal for someone who enjoys solving real customer problems, working with data and systems, understanding accounting workflows, and getting things across the finish line.

What You’ll Do

• Own the end-to-end implementation of the product for new and existing customers.
• Understand customer business processes, accounting workflows, and operational requirements.
• Configure customer/entity setups, workflows, recipes, rules, and product features based on business requirements.
• Work with accounting and finance teams to understand their requirements and translate them into product configurations.
• Set up and validate integrations with systems such as accounting platforms, POS systems, banking platforms, and other third-party systems.
• Investigate implementation issues, data discrepancies, integration failures, and workflow problems.
• Work closely with Engineering and Product teams to troubleshoot issues and drive them to resolution.
• Clearly communicate customer requirements, implementation blockers, and product gaps to internal teams.
• Coordinate across multiple stakeholders and ensure implementation tasks are completed on time.
• Test configurations and integrations end-to-end before customer rollout.
• Perform data validation and reconciliation to ensure the system is producing accurate results.
• Support customer/accounting teams during onboarding and transition them successfully to the product.
• Create implementation documentation, playbooks, and repeatable processes.
• Identify recurring customer problems and work with Product/Engineering to improve the product and implementation process.
• Provide feedback from customer implementations to help shape product improvements.

What We’re Looking For

• 2–6 years of experience in implementation, solutions engineering, technical consulting, customer success engineering, business systems, or a similar role.
• Strong problem-solving and analytical skills.
• Comfortable working directly with customers and internal technical teams.
• Ability to understand complex business processes and translate them into system configurations.
• Strong understanding of data, workflows, integrations, and APIs.
• Good understanding of accounting/finance concepts and workflows is highly preferred.
• Comfortable working with spreadsheets and data; SQL knowledge is a strong plus.
• Ability to investigate issues independently and identify the root cause rather than simply escalating problems.
• Strong ownership mindset — you take a problem from "this is broken" to "this is solved."
• Excellent communication skills, both written and verbal.
• Comfortable working in a fast-moving environment where requirements can evolve.
• Ability to manage multiple customer implementations and prioritize effectively.

Good to Have

• Experience with accounting systems such as NetSuite, QuickBooks Online, or similar platforms.
• Experience working with POS systems such as Toast or similar platforms.
• Experience with banking/payment integrations.
• Experience working with REST APIs and integration troubleshooting.
• SQL/Postgres experience.
• Experience working with accounting data, reconciliations, journal entries, revenue, expenses, payouts, or financial reporting.
• Experience in SaaS implementation or B2B software.
• Experience working with Engineering/Product teams in a startup environment.

What This Role Is NOT

This is not a traditional software engineering role where your primary responsibility is writing application code.

You will work closely with Engineering, but your primary responsibility is to:

Understand → Configure → Implement → Validate → Troubleshoot → Deploy → Drive Adoption

You should be comfortable getting hands-on with the product, customer data, integrations, and workflows to solve problems.

Who Will Succeed in This Role?

You will do well if you are someone who:

• Enjoys solving ambiguous problems.
• Can speak comfortably with both customers and engineers.
• Is technically curious and can understand how systems and integrations work.
• Doesn't wait for someone else to solve a problem.
• Can dig into data to find what is actually happening.
• Is comfortable working with accounting/finance teams.
• Enjoys taking ownership of implementations.
• Can balance customer requirements with product capabilities.
• Is willing to get hands-on and figure things out rather than simply following a checklist.

Why Join Us?

You will have significant ownership over how customers successfully adopt the product.

You will work across customers, accounting teams, Product, and Engineering, giving you exposure to both business and technology.

This role is a great opportunity for someone who wants to grow into a Solutions Engineer, Implementation Lead, Product Specialist, or Forward Deployed Engineer while working on real-world customer problems.

1. Company Description:

Tatvic, is a marketing analytics company focusing on generating insights from data using long association with Google & its infrastructure. We breed,recognize and reward performance. As a company we are growing very fast & we are under transformation. To enable this transformation we need future leaders with eyesight which balances execution & strategic understanding.

Website: www.tatvic.com

2. Role Details

• Role: Lead Data Engineer
• Band: L4
• Department: Data Engineering
• Reporting to: L5 / Head of Delivery
• Location: Ahmedabad
• Job Type: Full Time
• Experience: 6-8 years of relevant experience
• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field.

3. Mission & Role Overview

• Mission: To create high-level organizational impact by leading a team to design and deliver scalable, high-performance cloud-native data solutions. The Lead Data Engineer (L4) is responsible for ensuring technical excellence—specifically in BigQuery optimization and secure GCP/AWS architectures—while driving the transition to Agentic AI workflows and creating value through the development of L1-L3 engineers.
• Role Overview: This is a pivotal leadership role on the Management Track, responsible for end-to-end data outcomes for the function. The Lead Engineer ensures the quality of engineering execution, manages team utilization and resource allocation, and serves as the primary technical representative in cross-functional forums with Marketing, Strategy, and Product teams.

4. Key Responsibilities

• Lead the design, development, and deployment of scalable and efficient cloud-based data pipelines and architectures.
• Build reusable data solutions and software components that serve multiple customers and use cases.
• Architect cloud environments using GCP, ensuring best practices in IAM, Security, and Networking.
• Conduct Exploratory Data Analysis (EDA) on clickstream and behavioral datasets to derive insights.
• Manage end-to-end ETL processes, including data extraction, transformation, and loading using Python, SQL, and Airflow.
• Design and build production-grade software (APIs, services, internal tools) with sound engineering practices — version control, testing, CI/CD, and code review.
• Extend data platforms with AI capabilities — integrating LLMs, RAG pipelines, and agentic workflows where they add clear business value.
• Perform Root Cause Analysis (RCA) and resolve data quality, pipeline, or application issues.
• Collaborate cross-functionally with Marketing, Insights, Product, and Engineering teams to align data initiatives with business goals.
• Document architectures, design decisions, and operations clearly and consistently.
• Coach and mentor team members, ensuring technical excellence and continuous learning.
• Participate in recruitment and knowledge-sharing through interviews, blogs, and webinars.

5. Must-Have & Good-to-Have Skills

Must-Have Skills:

• Strong understanding of clickstream data and data pipeline architecture
• Hands-on experience with cloud platforms (GCP)
• Expertise in SQL, Python, and Airflow
• Solid software engineering foundation — API design, testing, version control, and deployment practices
• Proven ability to perform exploratory data analysis and data validation
• Knowledge of cloud security, IAM, and networking best practices
• Working knowledge of building or integrating AI/LLM-based components into existing systems
• Excellent problem-solving, stakeholder management, and communication skills

Good-to-Have Skills:

• Certifications such as: Google Cloud Professional Data Engineer / Cloud Architect
• AWS Certified Data Analytics or Azure Data Engineer Associate
• Google Analytics 4 (GAIQ)
• Experience in marketing attribution, commerce data, or analytics ecosystems
• Exposure to cost optimization and cloud resource management
• Experience with agent frameworks (LangChain, ADK, MCP) or LLM APIs
• Experience building automation frameworks or internal data tools

6. Not-a-Fit Criteria

• Individual Contributor Focus: Operates as the team's best individual contributor instead of building team capability or empowering L3s.
• Information Hoarding: Keeps architectural patterns or tribal knowledge trapped in their head rather than creating reusable playbooks and documentation.
• Avoidance of Conflict: Avoids difficult performance conversations with underperforming team members, allowing capability gaps to persist.
• Activity Over Outcomes: Measures success by "tasks completed" rather than pipeline stability, data accuracy, or business impact.
• Functional Siloing: Fails to build partnerships with Marketing or Product teams, leading to misaligned data delivery.

Our Culture & Perks - Where Passion Meets Purpose:

• Competitive CTC: Based on experience and skillset
• Medical Insurance for you and your family
• Return-to-Work Program for New Moms — because career breaks shouldn’t break momentum
• 1 Work From Home Day/Month — flexibility when life calls
• 2 Birthday Leaves/Year — one for you, one for someone you love
• Monthly Engagement Activities — from game nights to learning sessions
• Inclusive Culture & Equal Opportunity Employer — diversity drives our innovation
• We Think Like Engineers & Athletes: we build, we iterate, we scale, we win — together
• 5-day workweek — because balance drives brilliance

Mandatory Skills (Must Have)

• SQL and Database Design (5+ years in enterprise data platforms)

• Python development for automation and platform services (5+ years)

• Linux administration and Shell Scripting in production environments

• Metadata-driven systems and data management platform experience

• Technical leadership of offshore or distributed development teams Wissen Technology

• System analysis, troubleshooting, and production support expertise

• Metadata onboarding, integration, and governance processes

• Stakeholder management and delivery ownership across multiple teams

Key Responsibilities

• Lead developers supporting the Pantheon Metadata platform and related services

• Own end-to-end delivery of metadata onboarding and enhancement initiatives

• Design scalable metadata management solutions and automation capabilities

• Collaborate with architecture, infrastructure, and application teams for enterprise integration

• Participate in solution design reviews and establish development best practices

• Prioritize enhancements with business stakeholders and manage delivery commitments

• Provide production support, troubleshooting, and platform stability improvements Wissen Technology

• Drive modernization initiatives related to metadata lifecycle management

Position: Technical Architect – Data Engineering

Job Summary:

• We are looking for an experienced Technical Architect to lead the design and implementation of modern cloud-based data platforms.
• The ideal candidate should have strong expertise in Azure and/or AWS, Databricks, Snowflake, modern data architecture, and large-scale data engineering.
• The candidate will work closely with business stakeholders, architects, and engineering teams to deliver scalable, secure, and high-performance data solutions.

Key Responsibilities

• Design enterprise-scale data lakehouse and data warehouse architectures.

• Define data ingestion, transformation, and serving architecture.

• Lead architecture discussions and technical governance.

• Design scalable ETL/ELT frameworks using Databricks and Snowflake.

• Define best practices for security, performance, CI/CD, and DevOps.

• Guide engineering teams on implementation.

• Collaborate with business, product, and data governance teams.

Must Have Skills :

• Azure or AWS

• Databricks

• Snowflake

• Data Lakehouse Architecture

• PySpark

• SQL

• Data Modelling

• ETL/ELT Architecture

• Performance Optimization

• CI/CD

• Terraform or Infrastructure as Code (preferred)

Preferred

• Insurance domain

• dbt

• Unity Catalog

• Delta Lake

• Iceberg

• Azure Data Factory / AWS Glue  

GCP Data Engineering Lead

Experience: 9+ Years

Lead Experience: 2+ Years

Location: Bangalore / Hyderabad

Key Skills:

• Strong experience in GCP Data Engineering and BigQuery
• Hands-on experience with Oracle Exadata / PL-SQL
• Strong knowledge of PySpark / Scala and Python
• Experience with GoldenGate, Kafka and CDC
• Hands-on experience with Apache Airflow
• Good experience in CI/CD and DevOps practices
• Experience with Terraform / Infrastructure as Code
• Exposure to AI/LLM technologies and GenAI solutions
• Strong understanding of data architecture, ETL/ELT and data pipelines

Roles & Responsibilities:

• Lead the design and development of scalable GCP data engineering solutions.
• Design and implement batch and real-time data pipelines using BigQuery, PySpark, Kafka/CDC and Airflow.
• Work with Oracle Exadata/PL-SQL and GoldenGate for data integration and migration.
• Implement CI/CD pipelines and infrastructure automation using Terraform.
• Explore and integrate AI/LLM capabilities into data engineering solutions.
• Lead technical discussions, code reviews, solution design and mentor team members.
• Collaborate with business and technical teams to deliver high-quality data solutions.